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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]

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]

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]

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]

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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 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]

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]

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. [ABSTRACT FROM AUTHOR]

The applicability of a blackbody source to sensitivity measurements and calibration of an uncooled Terahertz (THz) focal plane array (FPA) is discussed, but the combination of a blackbody source and a band-pass filter may not be suitable for these purposes. Two ways to measure the minimum detectable power (MDP) of uncooled THz-FPAs are described and compared when used with strong THz sources such as a quantum cascade laser (QCL). The MDP is defined as the radiant power that produces a signal-to-noise ratio of unity in the output of a THz imager that uses a THz-FPA operating at a TV frame rate. One method (method 1) is based on the hypothesis that the beam pattern of the THz source is close to a Gaussian pattern. In another method (method 2), the signal level of the background in an image that does not contain a THz source is subtracted from the signal of the image. While method 2 is more flexible, how large the signals coming from THz source should be, as compared with the noise level, remains to be defined. Finally, based on issues with the current non-uniformity correction (NUC) technique, specifications are proposed for THz source power and wobbling technique to obtain uniform illumination for an ideal NUC technique. [ABSTRACT FROM AUTHOR]

Non-uniformity (NU) in infrared images can cause great degradation of the image quality and appearance. Scene-based nonuniformity correction (SBNUC) has become a very effective way to deal with NU. Although many SBNUC methods have been developed by researchers worldwide, few of them have a good correction performance and can be applied to small-package real-time devices. In this paper, we propose a time-domain projection-based registrationscene-based NU correction technology and its detailed hardware realization. We developed a new projection estimator to calculate the relative displacement of neighboring frames. The estimator uses a column and row projection vector to calculate the displacement separately without reducing the accuracy. We also developed an improved gain coefficient correction method using the corrected bias coefficient to correct the gain coefficient by clarifying the intrinsic relationship between these two coefficients instead of correcting them separately. We have also thoroughly analyzed how this technology performs with an actual infrared video sequence containing both low-frequency and high-frequency NUs. The hardware realization of this technology in a single-FPGA-core real-time system is also described in detail. We have successfully realized this technology in a real engineering application. Detailed flow charts for the hardware implementation of this algorithm are also provided. [ABSTRACT FROM AUTHOR]

Passive millimeter wavelength (PMMW) video holds great promise, given its ability to see targets and obstacles through fog, smoke, and rain. However, current imagers produce undesirable complex noise. This can come as a mixture of fast shot (snowlike) noise and a slower-forming circular fixed pattern. Shot noise can be removed by a simple gain style filter. However, this can produce blurring of objects in the scene. To alleviate this, we measure the amount of Bayesian surprise in videos. Bayesian surprise measures feature change in time that is abrupt but cannot be accounted for as shot noise. Surprise is used to attenuate the shot noise filter in locations of high surprise. Since high Bayesian surprise in videos is very salient to observers, this reduces blurring, particularly in places where people visually attend. Fixed pattern noise is removed after the shot noise using a combination of non-uniformity correction and mean image wavelet transformation. The combination allows for online removal of time-varying fixed pattern noise, even when background motion may be absent. It also allows for online adaptation to differing intensities of fixed pattern noise. We also discuss a method for sharpening frames using deconvolution. The fixed pattern and shot noise filters are all efficient, which allows real time video processing of PMMW video. We show several examples of PMMW video with complex noise that is much cleaner as a result of the noise removal. Processed video clearly shows cars, houses, trees, and utility poles at 20 frames per second. [ABSTRACT FROM AUTHOR]

This paper presents the design and electro-optical test of a 160\times120-pixels CMOS sensor specifically conceived for Time-Of-Flight 3D imaging. The in-pixel processing allows the implementation of Indirect Time-Of-Flight technique for distance measurement with reset noise removal through Correlated Double Sampling and embedded fixed-pattern noise reduction, whereas a fast readout operation allows the pixels values to be streamed out at a maximum rate of 10 MSample/s. The imager can operate as a fast 2D camera up to 458 fps, as a 3D camera up to 80 fps, or even coupling both operation modes. The chip has been fabricated using a standard 0.18 \mu\m 1P4M 1.8 V CMOS technology with MIM capacitors. The resulting pixel has a pitch of 29.1 \mu\m with a fill-factor of 34% and includes 66 transistors. Distance measurements up to 4.5 m have been performed with pulsed laser light, achieving a best precision of 10 cm at 1 m in real-time at 55 fps and 175 mA current consumption. [ABSTRACT FROM AUTHOR]

The workflow for estimating the temperature in agricultural fields from multiple sensors needs to be optimized upon testing each type of sensor's actual user performance. In this sense, readily available miniaturized UAV-based thermal infrared (TIR) cameras can be combined with proximal sensors in measuring the surface temperature. Before the two types of cameras can be operationally used in the field, laboratory experiments are needed to fully understand their capabilities and all the influencing factors. We present the measurement results of laboratory experiments of UAV-borne WIRIS 2nd GEN and handheld FLIR E8-XT cameras. For these uncooled sensors, it took 30 to 60 min for the measured signal to stabilize and the sensor temperature drifted continuously. The drifting sensor temperature was strongly correlated to the measured signal. Specifically for WIRIS, the automated non-uniformity correction (NUC) contributed to extra uncertainty in measurements. Another problem was the temperature measurement dependency on various ambient environmental parameters. An increase in the measuring distance resulted in the underestimation of surface temperature, though the degree of change may also come from reflected radiation from neighboring objects, water vapor absorption, and the object size in the field of view (FOV). Wind and radiation tests suggested that these factors can contribute to the uncertainty of several Celsius degrees in measured results. Based on these indoor experiment results, we provide a list of suggestions on the potential practices for deriving accurate temperature data from radiometric miniaturized TIR cameras in actual field practices for (agro-)environmental research. [ABSTRACT FROM AUTHOR]

We present experimental methods and results for photo-response non-uniformity correction (PRNUC) in range for a 3D flash LiDAR camera from non-optimal static-scene calibration data. Range walk is also corrected. This method breaks up the camera's focal plane array (FPA) into 16 × 16 windowed regions of interest that are incrementally captured and stitched together in post-processing across the entire FPA. The illumination was not uniform, thus requiring additional methods described by our paper to create an acceptable correction. We present the results from a full non-uniformity correction and range walk error correction processed for a set of independently collected validation frames; these validation frames used identical experimental conditions and the same target as was collected for the corrections. We will show that this experimental approach improves range accuracy and range precision of the corrected validation frames despite the sub-optimal conditions of the data used to compute the corrections; the single shot range precision is corrected to 33 cm, as compared to a modeled precision of 15.65 cm, while the accuracy is corrected to 252 cm. This method has implications for simplification of characterization of non-uniformity and range walk error, and its subsequent correction, in 3D flash LiDAR cameras. [ABSTRACT FROM AUTHOR]

The non-uniform response in infrared focal plane array (IRFPA) detectors inevitably produces corrupted images with a fixed-pattern noise. In this paper, we present a novel and adaptive scene-based non-uniformity correction (NUC) method called Correction method with Statistical scene-based and Interframe Registration (CSIR), which realizes low delay calculation of correction coefficient for infrared image. This method combines the statistical method and registration method to achieve a better NUC performance. Specifically, CSIR estimates the gain coefficient with statistical method to give registration method an appropriate initial value. This combination method not only reduces the need of interactive pictures, which means lower time delay, but also achieves better performance compared to the statistical method and other single registration methods. To verify this, real non-uniformity infrared image sequences collected by ourselves were used, and the advantage of CSIR was compared thoroughly on frame number (corresponding to delay time) and accuracy. The results show that the proposed method could achieve a significantly fast and reliable fixed-pattern noise reduction with the effective gain and offset. [ABSTRACT FROM AUTHOR]