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In this article, the authors propose a general framework for checking the calibration of the A325sc camera and then minimizing the temperature measurement errors due to thermal drift. Thermal drift and non-uniformity affect the measurement accuracy of infrared bolometric cameras and remain a major problem for the reproducibility and repeatability of radiance quantification. Any thermal camera is pre-calibrated at the factory to correct for thermal drift. This pre-calibration was done for specific case temperatures, and variations in these temperatures are a major source of uncertainty. To improve the accuracy of the camera measurements, it's important to control the housing temperatures. To this end, a cold box was build up. The effectiveness of the thermal drift compensation was examined over the two ranges of the camera. In the [-20°C; 120°C] range, the thermal drift compensation is efficient up to 110°C. The range [0°C; 350°C] highlights two behaviors: for an emitting temperature within [65-225] °C, the thermal drift compensation is made difficult by the self-heating of the measurement chain due to the intensity of the source. Above 225°C, the self-heating of the optics is significant, as it becomes more absorbent. A correction of the thermosignal is suggested. [ABSTRACT FROM AUTHOR]

The inhomogeneity of spectral pixel response is an unavoidable phenomenon in hyperspectral imaging, which is mainly manifested by the existence of inhomogeneity banding noise in the acquired hyperspectral data. It must be carried out to get rid of this type of striped noise since it is frequently uneven and densely distributed, which negatively impacts data processing and application. By analyzing the source of the instrument noise, this work first created a novel non-uniform noise removal method for a spatial dimensional push sweep hyperspectral imaging system. Clean and clear medical hyperspectral brain tumor tissue images were generated by combining scene-based and reference-based non-uniformity correction denoising algorithms, providing a strong basis for further diagnosis and classification. The precise procedure entails gathering the reference dark background image for rectification and the actual medical hyperspectral brain tumor image. The original hyperspectral brain tumor image is then smoothed using a weighted least squares algorithm model embedded with bilateral filtering (BLF-WLS), followed by a calculation and separation of the instrument fixed-mode fringe noise component from the acquired reference dark background image. The purpose of eliminating non-uniform fringe noise is achieved. In comparison to other common image denoising methods, the evaluation is based on the subjective effect and unreferenced image denoising evaluation indices. The approach discussed in this paper, according to the experiments, produces the best results in terms of the subjective effect and unreferenced image denoising evaluation indices (MICV and MNR). The image processed by this method has almost no residual non-uniform noise, the image is clear, and the best visual effect is achieved. It can be concluded that different denoising methods designed for different noises have better denoising effects on hyperspectral images. The non-uniformity denoising method designed in this paper based on a spatial dimension push-sweep hyperspectral imaging system can be widely used. [ABSTRACT FROM AUTHOR]

Stripe noise is a prevalent issue in infrared imaging systems, characterized by its distinctive directional features, which often appear as vertical lines across the image. This type of noise can significantly degrade the quality of the captured images, making it crucial to address and mitigate its effects. This paper presents an effective strategy to tackle this problem by transforming it from a 2D image issue into a 1D signal problem, enabling efficient resolution of stripes in infrared images. By understanding the characteristics of stripe noise, the proposed algorithm effectively solves the problem by first computing the column average of the noisy image, extracting stripe components from this one-dimensional signal, and effectively removing the stripes without blurring image details. This approach has been tested on numerous images with varying noise levels, demonstrating exceptional denoising performance compared to state-of-the-art methods. The results show marked improvements in visual quality, especially around edges and smooth areas, without requiring complex algorithms or iterative processes. [ABSTRACT FROM AUTHOR]

The inhomogeneity of spectral pixel response is an unavoidable phenomenon in hyperspectral imaging, which is mainly manifested by the existence of inhomogeneity banding noise in the acquired hyperspectral data. It must be carried out to get rid of this type of striped noise since it is frequently uneven and densely distributed, which negatively impacts data processing and application. By analyzing the source of the instrument noise, this work first created a novel non-uniform noise removal method for a spatial dimensional push sweep hyperspectral imaging system. Clean and clear medical hyperspectral brain tumor tissue images were generated by combining scene-based and reference-based non-uniformity correction denoising algorithms, providing a strong basis for further diagnosis and classification. The precise procedure entails gathering the reference dark background image for rectification and the actual medical hyperspectral brain tumor image. The original hyperspectral brain tumor image is then smoothed using a weighted least squares algorithm model embedded with bilateral filtering (BLF-WLS), followed by a calculation and separation of the instrument fixed-mode fringe noise component from the acquired reference dark background image. The purpose of eliminating non-uniform fringe noise is achieved. In comparison to other common image denoising methods, the evaluation is based on the subjective effect and unreferenced image denoising evaluation indices. The approach discussed in this paper, according to the experiments, produces the best results in terms of the subjective effect and unreferenced image denoising evaluation indices (MICV and MNR). The image processed by this method has almost no residual non-uniform noise, the image is clear, and the best visual effect is achieved. It can be concluded that different denoising methods designed for different noises have better denoising effects on hyperspectral images. The non-uniformity denoising method designed in this paper based on a spatial dimension push-sweep hyperspectral imaging system can be widely used.

This paper presents an FPGA-based lightweight and real-time infrared image processor based on a series of hardware-oriented lightweight algorithms. The two-point correction algorithm based on blackbody radiation is introduced to calibrate the non-uniformity of the sensor. With precomputed gain and offset matrices, the design can achieve real-time non-uniformity correction with a resolution of 640 × 480 . The blind pixel detection algorithm employs the first-level approximation to simplify multiple iterative computations. The blind pixel compensation algorithm in our design is constructed on the side-window-filtering method. The results of eight convolution kernels for side windows are computed simultaneously to improve the processing speed. Due to the proposed side-window-filtering-based blind pixel compensation algorithm, blind pixels can be effectively compensated while details in the image are preserved. Before image output, we also incorporated lightweight histogram equalization to make the processed image more easily observable to the human eyes. The proposed lightweight infrared image processor is implemented on Xilinx XC7A100T-2. Our proposed lightweight infrared image processor costs 10,894 LUTs, 9367 FFs, 4 BRAMs, and 5 DSP48. Under a 50 MHz clock, the processor achieves a speed of 30 frames per second at the cost of 1800 mW. The maximum operating frequency of our proposed processor can reach 186 MHz. Compared with existing similar works, our proposed infrared image processor incurs minimal resource overhead and has lower power consumption. [ABSTRACT FROM AUTHOR]

In the context of non-uniformity correction (NUC) within infrared imaging systems, current methods frequently concentrate solely on high-frequency stripe non-uniformity noise, neglecting the impact of global low-frequency non-uniformity on image quality, and are susceptible to ghosting artifacts from neighboring frames. In response to such challenges, we propose a method for the correction of non-uniformity in single-frame infrared images based on noise separation in the wavelet domain. More specifically, we commence by decomposing the noisy image into distinct frequency components through wavelet transformation. Subsequently, we employ a clustering algorithm to extract high-frequency noise from the vertical components within the wavelet domain, concurrently employing a method of surface fitting to capture low-frequency noise from the approximate components within the wavelet domain. Ultimately, the restored image is obtained by subtracting the combined noise components. The experimental results demonstrate that the proposed method, when applied to simulated noisy images, achieves the optimal levels among seven compared methods in terms of MSE, PSNR, and SSIM metrics. After correction on three sets of real-world test image sequences, the average non-uniformity index is reduced by 75.54%. Moreover, our method does not impose significant computational overhead in the elimination of superimposed noise, which is particularly suitable for applications necessitating stringent requirements in both image quality and processing speed. [ABSTRACT FROM AUTHOR]

As the demand for thermal information increases in industrial fields, numerous studies have focused on enhancing the quality of infrared images. Previous studies have attempted to independently overcome one of the two main degradations of infrared images, fixed pattern noise (FPN) and blurring artifacts, neglecting the other problems, to reduce the complexity of the problems. However, this is infeasible for real-world infrared images, where two degradations coexist and influence each other. Herein, we propose an infrared image deconvolution algorithm that jointly considers FPN and blurring artifacts in a single framework. First, an infrared linear degradation model that incorporates a series of degradations of the thermal information acquisition system is derived. Subsequently, based on the investigation of the visual characteristics of the column FPN, a strategy to precisely estimate FPN components is developed, even in the presence of random noise. Finally, a non-blind image deconvolution scheme is proposed by analyzing the distinctive gradient statistics of infrared images compared with those of visible-band images. The superiority of the proposed algorithm is experimentally verified by removing both artifacts. Based on the results, the derived infrared image deconvolution framework successfully reflects a real infrared imaging system. [ABSTRACT FROM AUTHOR]

Non-uniformity in the response of spectral image elements is an inevitable phenomenon in hyperspectral imaging, which mainly manifests itself as the presence of band noise in the acquired hyperspectral data. This problem is prominent in the infrared band owing to the detector material, operating environment, and other factors. Non-uniformity is an important factor that can affect the quality of the hyperspectral data, which has a serious impact on both data analysis and applications and requires corrections via technical means wherever possible. This paper proposes a novel target-based non-uniformity self-correction method for infrared push-broom hyperspectral images. The Mars Mineralogical Spectrometer (MMS) onboard the Tianwen-1 orbiter was used as the research and application object. The model is constructed and applied to the target scene characteristics and detection patterns of Mars remote sensing exploration, which are combined with the causes of noise generation in the infrared spectral image bands. The design of the MMS dual-channel Visible-Near-Infrared (V-NIR) and Near-Mid-Infrared (N-MIR) co-field of view co-target detection and laboratory calibration data for the V-NIR spectral band can achieve non-uniformity corrections (NUCs). Therefore, for the MMS in-orbit Mars exploration mission, the method selected spectral data (920–1055 nm) characterized by a reduced atmospheric influence to iteratively obtain the homogeneous region, which was used to calculate the non-uniformity correction factor for the N-MIR spectral band. This method was compared, validated, and evaluated with other conventional methods using both laboratory and in-orbit hyperspectral data. The results showed that the experimental data corrections were comparable to laboratory calibrations, with a maximum relative deviation of <2.6%. These results prove that our method not only provides an excellent non-uniformity correction, but also ensures spectral fidelity. It can thus be used as a non-uniformity correction process for the MMS and similar hyperspectral imagers. [ABSTRACT FROM AUTHOR]

Non-uniformity commonly exists in the infrared focal plane, which behaves as the fixed-pattern noise (FPN) and seriously affects the image quality of long-wave infrared (LWIR) detection systems. The two-point correction (TPC) method is commonly used to reduce image FPN in engineering. However, when a wide-spectrum LWIR detection system calibrated with a black body is used to detect weak and small targets in the sky, FPN still appears in the image, affecting its uniformity. The effects of atmospheric transmittance characteristics of long-range paths on the non-uniformity of wide-spectrum long-wave infrared systems have not been studied. This paper proposes a modified TPC model based on spectral subdivision that introduces atmospheric transmittance. Additionally, the effects of atmospheric transmittance characteristics on the long-wave infrared non-uniform correction coefficient are analyzed. The experimental results for a black body scene and sky scene using a weak and small target detection system with a long-wave Sofradir FPA demonstrate that the wide-spectrum LWIR detection system fully considers atmospheric transmittance when performing calibration based on the TPC method, which can reduce the non-uniformity of the image. [ABSTRACT FROM AUTHOR]

The non-uniformity of the readout circuit response in the infrared focal plane array unit detector can result in fixed pattern noise with stripe, which seriously affects the quality of the infrared images. Considering the problems of existing non-uniformity correction, such as the loss of image detail and edge blurring, a multi-scale residual network with attention mechanism is proposed for single infrared image stripe noise removal. A multi-scale feature representation module is designed to decompose the original image into varying scales to obtain more image information. The product of the direction structure similarity parameter and the Gaussian weighted Mahalanobis distance is used as the similarity metric; a channel spatial attention mechanism based on similarity (CSAS) ensures the extraction of a more discriminative channel and spatial feature. The method is employed to eliminate the stripe noise in the vertical and horizontal directions, respectively, while preserving the edge texture information of the image. The experimental results show that the proposed method outperforms four state-of-the-art methods by a large margin in terms of the qualitative and quantitative assessments. One hundred infrared images with different simulated noise intensities are applied to verify the performance of our method, and the result shows that the average peak signal-to-noise ratio and average structural similarity of the corrected image exceed 40.08 dB and 0.98, respectively. [ABSTRACT FROM AUTHOR]

This paper describes a new readout integrated circuit and non-uniformity correction (NUC) method that ensures that the bolometer array has low non-uniformity over a wide operating temperature range without a thermoelectric cooler (TEC). The proposed NUC minimizes the circuit and memory required for signal processing, making it suitable for compact and power-efficient portable infrared cameras. It corrects the aging phenomenon through start-up calibration and corrects non-uniformities without a TEC through calibration during operation mode. It minimizes the calibration process during operation mode and uses a pixel-level analog-to-digital converter to enable real-time NUC. A 0.18 μm standard CMOS process is applied to the proposed NUC. The frame rate for calibration during the operation mode is approximately 14.3 Hz. The proposed NUC demonstrates excellent uniformity with a non-uniformity of less than 0.12% over a wide operating temperature range (−20 to 50 °C). [ABSTRACT FROM AUTHOR]

Non-uniformity is a common phenomenon in infrared imaging system, which seriously affects imaging quality. In view of the problems of existing non-uniformity correction of infrared images, such as loss of image details and blurred edge of image, an improved non-uniformity correction method of infrared images based on convolution neural network using long-short connections (LSC-CNN) is proposed. The proposed method designs a long-short connection residual network structure suitable for non-uniformity correction of infrared image.The network depth is increased to fully learn the noise by short connections, image sizes are adjusted to reduce the number of parameters, the long connection is used to solve the problem of image information loss caused by transposed convolution, and a multiply operation is carried out to enhance the contrast of corrected images. Besides, batch normalization is utilized to improve the training speed. The experimental results show that LSC-CNN has excellent performance in non-uniformity correction of infrared images whether qualitative evaluation or quantitative evaluation. LSC-CNN is especially effective in image detail preservation and image edge protection whose average PSNR exceeds 37.5 dB and the average SSIM is greater than 0.98.

In this study, we propose a thermal design for an on-board blackbody (BB) for spaceborne infrared (IR) sensor calibration. The main function of the on-board BB is to provide highly uniform and precise radiation temperature reference sources from 0 °C to 40 °C during the calibration of the IR sensor. To meet the functional requirements of BB, a BB thermal design using a heater to heat the BB during sensor calibration and heat pipes to transfer residual heat to the radiator after calibration is proposed and investigated both numerically and experimentally. The main features of the proposed thermal design are a symmetric temperature gradient on the BB surface with less than 1 K temperature uniformity, ease of temperature sensor implementation to estimate the representative surface temperature of the BB, a stable thermal interface between the heat pipes and BB, and a fail-safe function under one heat pipe failure. The thermal control performance of the BB is investigated via in-orbit thermal analysis, and its effectiveness is verified via a heat-up test of the BB under ambient conditions. These results indicate that the temperature gradient on the BB surface was obtained at less than 1 K, and the representative surface temperature could be estimated with an accuracy of 0.005 °C via the temperature sensor. [ABSTRACT FROM AUTHOR]

Abstract Stripe non-uniformity severely affects the quality of infrared images. It is challenging to remove stripe noise in low-texture images without blurring the details. We propose a single-frame image stripe correction algorithm that removes infrared noise while preserving image details. Firstly, wavelet transform is used for multi-scale analysis of the image. At the same time, Total variation model is used for small window to smooth the original image. The small-scale total variation model can well preserve the edge information of the image, but it will leave stripe noise. Therefore, according to the prior knowledge of the vertical component of the stripe noise, the spatial filtering is finally performed: the smoothed image is used as the guide image for the stripe noise denoising. It is possible to prevent the lead filter from mistaking the strong stripe noise as edge detail, resulting in corrected image residual streak noise. The algorithm is systematically evaluated by experiments on simulated images and original infrared images, as well as compared with the current advanced infrared stripe non-uniformity correction algorithms. It is proved that our algorithm can better eliminate stripe noise and preserve edge details.

Non-uniformity is a common phenomenon in infrared imaging system, which seriously affects imaging quality. In view of the problems of existing non-uniformity correction of infrared images, such as loss of image details and blurred edge of image, an improved non-uniformity correction method of infrared images based on convolution neural network using long-short connections (LSC-CNN) is proposed. The proposed method designs a long-short connection residual network structure suitable for non-uniformity correction of infrared image.The network depth is increased to fully learn the noise by short connections, image sizes are adjusted to reduce the number of parameters, the long connection is used to solve the problem of image information loss caused by transposed convolution, and a multiply operation is carried out to enhance the contrast of corrected images. Besides, batch normalization is utilized to improve the training speed. The experimental results show that LSC-CNN has excellent performance in non-uniformity correction of infrared images whether qualitative evaluation or quantitative evaluation. LSC-CNN is especially effective in image detail preservation and image edge protection whose average PSNR exceeds 37.5 dB and the average SSIM is greater than 0.98. [ABSTRACT FROM AUTHOR]

Non-uniformity commonly exists in the infrared focal plane, which behaves as the fixed pattern noise (FPN) and seriously affects the image quality of the infrared imaging system. This paper proposed a novel scene-based non-uniformity correction method with a new edge-preserve filter and adaptive learning rate. First, using co-occurrence filter as the desired image estimation, the proposed method removed the FPN while preserving the image details. Then, an adaptive learning rate connected with both temporal motion and spatial correlation factor is utilized to decrease the effect of ghosting artifacts. In this way, the proposed method overcomes the shortcomings of the traditional scene-based non-uniformity. Several real infrared image sequences collected in different conditions are used to verify the performance of the proposed method. The experimental results demonstrate that the proposed method has a much better visual effect, making a great balance between the non-uniformity correction and details preservation. Compared with other good NUC methods, this method also has better performance in the aspects of applicability and robustness, which has great application value.

In this study, we propose a thermal design for an on-board blackbody (BB) for spaceborne infrared (IR) sensor calibration. The main function of the on-board BB is to provide highly uniform and precise radiation temperature reference sources from 0 °C to 40 °C during the calibration of the IR sensor. To meet the functional requirements of BB, a BB thermal design using a heater to heat the BB during sensor calibration and heat pipes to transfer residual heat to the radiator after calibration is proposed and investigated both numerically and experimentally. The main features of the proposed thermal design are a symmetric temperature gradient on the BB surface with less than 1 K temperature uniformity, ease of temperature sensor implementation to estimate the representative surface temperature of the BB, a stable thermal interface between the heat pipes and BB, and a fail-safe function under one heat pipe failure. The thermal control performance of the BB is investigated via in-orbit thermal analysis, and its effectiveness is verified via a heat-up test of the BB under ambient conditions. These results indicate that the temperature gradient on the BB surface was obtained at less than 1 K, and the representative surface temperature could be estimated with an accuracy of 0.005 °C via the temperature sensor.

Copyright of Laser Technology is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• A new ring perturbation non-uniformity correction is proposed. • The proposed correction method effectively removes the non-uniformity of the image. • Compared with other methods, the proposed method achieved a noticeable improvement. To improve the detection ability of dim targets in strong clutter background, a new non-uniform correction method of ring perturbation in sequence images is proposed to remove the non-uniformity of images, and then the multi-frame cumulation technique is used to enhance the dim targets. Finally, on the basis of pre-processing, Poisson distribution is used to obtain candidate target images in advance, next use the high-order cumulant for further extract, and to process the detection results of the above two algorithms by "AND" operation to obtain the common candidate target points. With that, the motion estimation was made on the dim target to make accumulation along the estimated direction in order to enhance the signal of the dim small target. Experiments show that the proposed ring perturbation correction method can effectively improve the local signal-to-noise ratio (SNR) of dim small targets; in sequence image detection, the proposed algorithm in this paper achieves better detection results than other algorithms, and can effectively detect dim small targets in strong clutter background. [ABSTRACT FROM AUTHOR]

In this paper we present a novel non-uniformity correction (NUC) method to remove column fixed-pattern noise (FPN), which is introduced by non-uniformity of on-chip column-parallel readout circuit in uncooled infrared focal plane array. We first define a new image statistic measurement, which is named as 1D horizontal differential statistics, to differentiate column FPN from structural edges, and further propose a filtering scheme to adaptively compute noise terms in structure and non-structure regions by applying different correction models. The proposed NUC technique combines the advantages of global- and local-based correction methods, thus can effectively eliminate column FPN without losing original thermal details. The performance of the proposed method is systematically evaluated, and is compared with the state-of-the-art column FPN correction solutions using realistic infrared images.

Abstract Stripe non-uniformity typically exists in infrared images and affects the visual effect; thus, eliminating stripe non-uniformity is essential to improve image quality. In this paper, a correction model with higher accuracy is developed. Unlike some other stripe non-uniformity correction methods using the same gain coefficient and offset parameters for the pixels of each column, different deviations of the correction parameters in the same column resulted by unsatisfactory preliminary non-uniformity correction are considered and are thought to be small and still relevant in space. The proposed method calculates the correction parameters for each pixel respectively based on the intrinsic spatial correlation between adjacent pixels in a column. What is more, an edge detection method is included. The experimental results indicate that the proposed algorithm effectively eliminates stripe noise of images of different scenes and it also works well in terms of preserving details. Furthermore, the algorithm exhibits high real-time performance. Highlights • Proposing a more accurate and simple single image stripe non-uniformity correction method for Infrared images. • Making use of the spatial correlation between pixels in the same column. • Adopting an edge detection method to avoid edge blurring. [ABSTRACT FROM AUTHOR]

Parallel imaging can be used to increase SNR and shorten acquisition times, albeit, at the cost of image non-uniformity. B 1 − non-uniformity correction techniques are confounded by signal that varies not only due to coil induced B 1 − sensitivity variation, but also the object's own intrinsic signal. Herein, we propose a method that makes minimal assumptions and uses only the coil images themselves to produce a single combined B 1 − non-uniformity-corrected complex image with the highest available SNR. A novel background noise classifier is used to select voxels of sufficient quality to avoid the need for regularization. Unique properties of the magnitude and phase were used to reduce the B 1 − sensitivity to two joint additive models for estimation of the B 1 − inhomogeneity. The complementary corruption of the imaged object across the coil images is used to abate individual coil correction imperfections. Results are presented from two anatomical cases: (a) an abdominal image that is challenging in both extreme B 1 − sensitivity and intrinsic tissue signal variation, and (b) a brain image with moderate B 1 − sensitivity and intrinsic tissue signal variation. A new relative Signal-to-Noise Ratio (rSNR) quality metric is proposed to evaluate the performance of the proposed method and the RF receiving coil array. The proposed method has been shown to be robust to imaged objects with widely inhomogeneous intrinsic signal, and resilient to poorly performing coil elements. [ABSTRACT FROM AUTHOR]

The non-uniformity correction (NUC) is an effective way to reduce fix pattern noise (FPN) and improve infrared image quality. The temporal high-pass NUC method is a kind of practical NUC method because of its simple implementation. However, traditional temporal high-pass NUC methods rely deeply on the scene motion and suffer image ghosting and blurring. Thus, this paper proposes an improved NUC method based on Shearlet Transform (ST). First, the raw infrared image is decomposed into multiscale and multi-orientation subbands by ST and the FPN component mainly exists in some certain high-frequency subbands. Then, high-frequency subbands are processed by the temporal filter to extract the FPN due to its low-frequency characteristics. Besides, each subband has a confidence parameter to determine the degree of FPN, which is estimated by the variance of subbands adaptively. At last, the process of NUC is achieved by subtracting the estimated FPN component from the original subbands and the corrected infrared image can be obtained by the inverse ST. The performance of the proposed method is evaluated with real and synthetic infrared image sequences thoroughly. Experimental results indicate that the proposed method can reduce heavily FPN with less roughness and RMSE. [ABSTRACT FROM AUTHOR]

The performance of SLP-THP based non-uniformity correction algorithm is seriously affected by the result of SLP filter, which always leads to image blurring and ghosting artifacts. To address this problem, an improved SLP-THP based non-uniformity correction method with curvature constraint was proposed. Here we put forward a new way to estimate spatial low frequency component. First, the details and contours of input image were obtained respectively by minimizing local Gaussian curvature and mean curvature of image surface. Then, the guided filter was utilized to combine these two parts together to get the estimate of spatial low frequency component. Finally, we brought this SLP component into SLP-THP method to achieve non-uniformity correction. The performance of proposed algorithm was verified by several real and simulated infrared image sequences. The experimental results indicated that the proposed algorithm can reduce the non-uniformity without detail losing. After that, a GPU based parallel implementation that runs 150 times faster than CPU was presented, which showed the proposed algorithm has great potential for real time application. [ABSTRACT FROM AUTHOR]

In the article a non-uniformity correction method is presented which allows to compensate for the influence of detector’s temperature drift. For this purpose, dependency between output signal value and the temperature of the detector array was investigated. Additionally the influence of the temperature on the Offset and Gain coefficients was measured. Presented method utilizes estimated dependency between output signal of detectors and their temperature. In the presented method, the shutter is used for establishing signal reference. Thermoelectric cooler is used for changing the temperature of the detector array.

In the uncooled infrared imaging systems, owing to the non-uniformity of the amplifier in the readout circuit, the infrared image has obvious stripe noise, which greatly affects its quality. In this study, the generation mechanism of stripe noise is analyzed, and a new stripe correction algorithm based on wavelet analysis and gradient equalization is proposed, according to the single-direction distribution of the fixed image noise of infrared focal plane array. The raw infrared image is transformed by a wavelet transform, and the cumulative histogram of the vertical component is convolved by a Gaussian operator with a one-dimensional matrix, in order to achieve gradient equalization in the horizontal direction. In addition, the stripe noise is further separated from the edge texture by a guided filter. The algorithm is verified by simulating noised image and real infrared image, and the comparison experiment and qualitative and quantitative analysis with the current advanced algorithm show that the correction result of the algorithm in this paper is not only mild in visual effect, but also that the structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) indexes can get the best result. It is shown that this algorithm can effectively remove stripe noise without losing details, and the correction performance of this method is better than the most advanced method.

In uncooled long-wave infrared (LWIR) imaging systems, non-uniformity of the amplifier in readout circuit will generate significant noise in captured infrared images. This type of noise, if not eliminated, may manifest as vertical and horizontal strips in the raw image and human observers are particularly sensitive to these types of image artifacts. In this paper we propose an effective non-uniformity correction (NUC) method to remove strip noise without loss of fine image details. This multi-scale destriping method consists of two consecutive steps. Firstly, wavelet-based image decomposition is applied to separate the original input image into three individual scale levels: large, median and small scales. In each scale level, the extracted vertical image component contains strip noise and vertical-orientated image textures. Secondly, a novel multi-scale 1D guided filter is proposed to further separate strip noise from image textures in each individual scale level. More specifically, in the small scale level, we choose a small filtering window for guided filter to eliminate strip noise. On the contrary, a large filtering window is used to better preserve image details from blurring in large scale level. Our proposed algorithm is systematically evaluated using real-captured infrared images and the quantitative comparison results with the state-of-the-art destriping algorithms demonstrate that our proposed method can better remove the strip noise without blurring image fine details. [ABSTRACT FROM AUTHOR]

A novel scene-based non-uniformity correction (NUC) method for short-wavelength infrared (SWIR) push-broom hyperspectral sensors is proposed and evaluated. This method relies on the assumption that for each band there will be ground objects with similar reflectance to form uniform regions when a sufficient number of scanning lines are acquired. The uniform regions are extracted automatically through a sorting algorithm, and are used to compute the corresponding NUC coefficients. SWIR hyperspectral data from airborne experiment are used to verify and evaluate the proposed method, and results show that stripes in the scenes have been well corrected without any significant information loss, and the non-uniformity is less than 0.5%. In addition, the proposed method is compared to two other regular methods, and they are evaluated based on their adaptability to the various scenes, non-uniformity, roughness and spectral fidelity. It turns out that the proposed method shows strong adaptability, high accuracy and efficiency. [ABSTRACT FROM AUTHOR]

The Non-uniformity of Infrared Focal Plane Arrays (IRFPA) severely degrades the infrared image quality. An effective non-uniformity correction (NUC) algorithm is necessary for an IRFPA imaging and application system. However traditional scene-based NUC algorithm suffers the image blurring and artificial ghosting. In addition, few effective hardware platforms have been proposed to implement corresponding NUC algorithms. Thus, this paper proposed an improved neural-network based NUC algorithm by the guided image filter and the projection-based motion detection algorithm. First, the guided image filter is utilized to achieve the accurate desired image to decrease the artificial ghosting. Then a projection-based moving detection algorithm is utilized to determine whether the correction coefficients should be updated or not. In this way the problem of image blurring can be overcome. At last, an FPGA-based hardware design is introduced to realize the proposed NUC algorithm. A real and a simulated infrared image sequences are utilized to verify the performance of the proposed algorithm. Experimental results indicated that the proposed NUC algorithm can effectively eliminate the fix pattern noise with less image blurring and artificial ghosting. The proposed hardware design takes less logic elements in FPGA and spends less clock cycles to process one frame of image. [ABSTRACT FROM AUTHOR]

Imaging non-uniformity of infrared focal plane array (IRFPA) behaves as fixed-pattern noise superimposed on the image, which affects the imaging quality of infrared system seriously. In scene-based non-uniformity correction methods, the drawbacks of ghosting artifacts and image blurring affect the sensitivity of the IRFPA imaging system seriously and decrease the image quality visibly. This paper proposes an improved neural network non-uniformity correction method with adaptive learning rate. On the one hand, using guided filter, the proposed algorithm decreases the effect of ghosting artifacts. On the other hand, due to the inappropriate learning rate is the main reason of image blurring, the proposed algorithm utilizes an adaptive learning rate with a temporal domain factor to eliminate the effect of image blurring. In short, the proposed algorithm combines the merits of the guided filter and the adaptive learning rate. Several real and simulated infrared image sequences are utilized to verify the performance of the proposed algorithm. The experiment results indicate that the proposed algorithm can not only reduce the non-uniformity with less ghosting artifacts but also overcome the problems of image blurring in static areas. [ABSTRACT FROM AUTHOR]

The development of magnetic resonance imaging (MRI) revolutionized both the medical and scientific worlds. A large variety of MRI options have generated a huge amount of image data to interpret. The investigation of a specific tissue in 3D or 4D MR images can be facilitated by image processing techniques, such as segmentation and registration. In this work, we provide a brief review of the principles and methods that are commonly applied to achieve superior tissue segmentation results in MRI. The impacts of MR image acquisition on segmentation outcome and the principles of selecting and exploiting segmentation techniques tailored for specific tissue identification tasks are discussed. In the end, two exemplary applications, breast and fibroglandular tissue segmentation in MRI and myocardium segmentation in short-axis cine and real-time MRI, are discussed to explain the typical challenges that can be posed in practical segmentation tasks in MRI data. The corresponding solutions that are adopted to deal with these challenges of the two practical segmentation tasks are thoroughly reviewed. [ABSTRACT FROM AUTHOR]

In this paper, we propose an adaptive unidirectional variational nonuniformity correction algorithm for fixed-pattern noise removal. The proposed algorithm is based on a unidirectional variational sparse model that makes use of unidirectional characteristics of stripe nonuniformity noise. The iteratively reweighted least squares (IRLS) technique is introduced to optimize the proposed correction model, which makes the proposed algorithm easy to implement with existing conjugate gradient method without introducing additional variables and parameters. Moreover, we derive a formula to automatically update the regularization parameter from the images. Comparative experimental results on real infrared images indicate that the proposed method can remove the stripe nonuniformity noise effectively while maintaining more useful image details. [ABSTRACT FROM AUTHOR]

Near infrared (NIR) hyperspectral imaging (HSI) has established itself as a powerful non-destructive tool for the chemical analysis of heterogeneous samples. However, one of the main disadvantages of NIR HSI is that the technique suffers from instrumentation-related problems, which in turn affect the acquired images. In general, focal plane array (FPA) based hyperspectral systems are affected by spatial and spectral non-uniform response, the presence of defective sensors (e.g. dead or saturated sensors), and temporal and spatial (e.g. dark current) noise. Another issue is each new camera system needs to be calibrated to assess its specific responses to light. To correct for these issues, we used known standards to measure the response of the sensors and capture the location of the field of view and defective sensors using linear and quadratic models. The parameters of these models were then used as input features for classification of sensor responses using a k -means algorithm. The results conclude that linear models are insufficiently precise for calibration but estimate sufficiently accurately the system’s response and functionality. Specifically, it was shown that the classification method discriminates non-responsive regions effectively. [ABSTRACT FROM AUTHOR]

Variable integral time is an effective means to realize the high dynamic range (HDR) of infrared system. Unfortunately, it would lead to serious residual non-uniformity. The relationship between the correction parameters (gain and offset) of two-point non-uniform correction（NUC）and integral time is derived based on the infrared system response model. The result shows that the gain parameters are independent of integral time, and only the bias parameters change with integral time. The correctness of the above conclusions is confirmed by testing it against standard two-point NUC method. The result can provide a reasonable evidence for NUC of infrared system with variable integral time, which only updates the offset parameters but maintain same gain parameters. [ABSTRACT FROM AUTHOR]

The quality of an infrared focal plane array camera (IRFPA) in terms of image sharpness is conditioned on the sophisticated manufacturing process of its sensor stage. That is, it is hard to build photo detectors with exactly the same response of electrical signal. This problem is known as "non-uniformity" in IR technology and it manifests itself as superimposed grid in the output image of the camera, termed as fixed pattern noise (FPN). This noise emerges since the weak electric signals from the detectors must undergo a high gain amplifier stage, thus magnifying their differences notoriously at the exit of the camera. To address this problem, the detector is characterized as a linear model with two parameters (gain and offset). To find these parameters and counteract this inequality we propose an algorithm based on a nonlinear digital filter extended from a simple yet consistent experimentally verified theoretical development of the standard method of Constant Statistics (CS). We demonstrate that the new filter compares favorably with CS standard, in terms of convergence speed and therefore prompt fading of ghosting artifact or "ghosting". Parameters of the proposed algorithm were adjusted and when it was tested with synthesized and real infrared video, high levels of correction was achieved, notoriously decreasing the non-uniformity. [ABSTRACT FROM AUTHOR]

Purpose : To evaluate the precision of measures of bone volume and bone volume fraction derived from high-resolution 3T MRI of proximal femur bone microarchitecture using non-uniformity correction. Methods : This HIPAA compliant, institutional review board approved study was conducted on six volunteers (mean age $$56\pm 13$$ years), and written informed consent was obtained. All volunteers underwent a 3T FLASH MRI hip scan at three time points: baseline, second scan same day (intra-scans), and third scan one week later (inter-scans). Segmentation of femur images and values for total proximal femur volume ( $$T$$ ), bone volume ( $$B$$ ), and bone volume fraction (BVF) were calculated using in-house developed software, FireVoxel. Two types of non-uniformity corrections were applied to images (N3 and BiCal). Precision values were calculated using absolute percent error (APE). Statistical analysis was carried out using one-sample one-sided t test to observe the consistency of the precision and paired t test to compare between the various methods and scans. Results : No significant differences in bone volume measurements were observed for intra- and inter-scans. When using non-uniformity correction and assessing all subjects uniformly at the level of the lesser trochanter, precision values overall improved, especially significantly ( $$p< 0.05$$ ) when measuring bone volume, $$B$$ . $$B$$ values using the combination of N3 or BiCal with CLT had a significant consistent APE values of less than 2.5 %, while BVF values were all consistently and significantly lower than 2.5 % APE. Conclusion: Our results demonstrate the precision of high-resolution 3D MRI measures were comparable to that of dual-energy X-ray absorptiometry. Additional corrections to the analysis technique by cropping at the lesser trochanter or using non-uniformity corrections helped to improve precision. The high precision values from these MRI scans provide evidence for MRI of the proximal femur as a promising tool for osteoporosis diagnosis and treatment. [ABSTRACT FROM AUTHOR]

This paper describes a new CMOS readout circuit, which makes the microbolometer focal plane array have low spatial noise over a wide operating temperature range without a thermoelectric cooler. The readout circuit corrects the nonuniformity of each microbolometer pixel by the proposed self-bias equalization technique. The proposed readout circuit adopting the self-bias equalization has a feedback loop that makes the readout circuit find the bias voltage for the correction of nonuniformity by itself. The proposed circuit was fabricated using a 0.35-μm standard CMOS process. The measured results of the fabricated chip show that the spatial noise is less than the allowed spatial noise for the equivalent temperature difference of 50 mK over a wide operating temperature range. [ABSTRACT FROM PUBLISHER]