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Automatic identification and localization of rebar from ground penetrating radar (GPR) data has been a research topic of great interest. This paper presents an automated rebar picking algorithm for GPR data of concrete bridge decks. The algorithm is based on the Limited and Simplified Hyperbolic Summation (LSHS) technique in which the width of migration is limited and a counter is used to check if a hyperbolic signature exist in a sub-region of GPR image. More specifically, after a time-zero correction, each pixel in the raw GPR image with a positive amplitude will be migrated, as it is usually done with the conventional hyperbolic summation (HS) technique. However, for each pixel to be migrated, the width of migration is limited in the horizontal direction and a unit value will be used for migration, instead of the true amplitude values of the pixels. In the obtained image, the pixels containing rebar peaks will normally have the intensity values close to the number of pixels corresponding to the width of migration and, therefore, can be picked. Whereas the method is rather straightforward and simple to develop, its implementation on GPR data of two concrete bridge decks has shown good results. First, the GPR condition maps obtained from the proposed algorithm were in a good agreement with those developed by the manual method of rebar picking. Second, the accuracy of the picking algorithm was 98.09% and 99.21% for the two decks, respectively. [ABSTRACT FROM AUTHOR]

The use of an image detector to receive grating images and measure angle displacement via image processing is a relatively new technique, which yields higher resolution and better precision than the traditional moiré fringe method. To improve the robustness of image-type angle measurement, this paper proposes a robust sub-pixel subdivision algorithm based on the least square method. Firstly, by analyzing the characteristic of grating image, a new subdivision algorithm is established based on the least square method. Secondly, the simulations of robustness are completed to prove the performance in theoretically. Lastly, the proposed algorithm is used in a typical image-type angle sensor to test the performance in real case. By test, the proposed method is shown to be more accurate and with better robust than the traditional algorithm (centroid algorithm). In a typical image-type angle sensor, it successfully achieves a resolution of 0.62″ (21-bit), 2 13 -fold subdivision resolution, and precision of 12.85″. The results presented here may provide a theoretical and technological foundation for further research on small-size, high-resolution photographic rotary encoders. [ABSTRACT FROM AUTHOR]

Hyperspectral unmixing is an important technique for remote sensing image exploitation. It aims to decompose a mixed pixel into a collection of spectrally pure components (called endmembers ), and their corresponding proportions (called fractional abundances ). In recent years, many studies have revealed that unmixing using spectral information alone does not sufficiently incorporate the spatial information in the remotely sensed hyperspectral image, as the pixels are treated as isolated entities without taking into account the existing local correlation among them. To address this issue, several spatial preprocessing methods have been developed to include spatial information in the spectral unmixing process. In this paper, we present a new spatial preprocessing method which presents several advantages over existing methods. The proposed method is derived from the Simple Linear Iterative Clustering (SLIC) method, which adapts the global search scope of the clustering into local regions. As a result, the spatial correlation and the spectral similarity are intrinsically incorporated at the clustering step, which results in O ( N ) computational complexity of the clustering procedure with N being the number of pixels in the image. First, a regional clustering is iteratively performed by using spatial and spectral information simultaneously. The obtained result is a set of clustered partitions that exhibit both spectral similarity and spatial correlation. Then, for each partition we select a subset of candidate pixels with high spectral purity. Finally, the obtained candidate pixels are gathered together and fed to a spectral-based endmember extraction method to extract the final endmembers and their corresponding fractional abundances. Our newly developed method naturally integrates the spatial and the spectral information to retain the most relevant endmember candidates. Our experimental results, conducted using both synthetic and real hyperspectral scenes, indicate that the proposed method can obtain accurate unmixing results with less than 0.5% of the number of pixels used by other state-of-the-art methods. This confirms the advantages of integrating spatial and spectral information for hyperspectral unmixing purposes. [ABSTRACT FROM AUTHOR]

Enhancement of hazy images or video is challenging task because of low contrast exhibited in them. Global contrast stretching methods have been successful in restoring contrasts but problems like overcompensation, truncation of pixel values amounting to loss of information tends to creep in. Artifacts may be introduced and images may loose its colorfulness. This paper presents an evolutionary enhancement method for restoring contrast in images or videos while preserving its colorfulness and brightness. The study proposes a novel histogram equalization method inspired by principles of water cycle algorithm. The proposed method first smoothes Y channel of YCbCr color space and divides input frame into two components using Otsu's 2D thresholding. A set of weighing constraints have been formulated and applied to both components individually in a controlled manner. Water cycle algorithm has been employed to exploit an optimal value of weighing factors for enforcement of constraints on individual components. A three dimension objective function has been designed to suitably perform equalization and control enhancement process. Experimental results show that proposed method is effective in removing haze like patterns in images and videos. [ABSTRACT FROM AUTHOR]

Enhancement of ancient manuscripts is a predominant field in the world of image processing. Binarization is the essential tool to recognize the text through OCR technologies for the preservation of the ancient manuscripts. This paper addresses the problem of enhancing and binarizing the deteriorated old manuscripts by a simple novel method called Log based binarization. The proposed method modifies the pixel intensity so as to better differentiate the black pixels and thus produce better binarization results, has been explored. Detailed comparison of existing thresholding algorithms with the new algorithm has been given. The inspiration for the new algorithm was drawn from the standard Niblack algorithm and Nick Algorithm. [ABSTRACT FROM AUTHOR]

This paper is an in-depth look at the problem of removing the blur from a complex motion-blurred star image. Accordingly, a simple yet effective l p ( 0 < p ≤ 1 ) -regularized deblurring method based on stars image intensity is proposed. The model builds on the principle that the intensity of clear star image is in accordance with Laplacian distribution or generalized p Gaussian distribution. Further, two algorithms are introduced to solve the ensuing non-smooth ( p = 1 ) or non-convex ( p < 1 ) constrained optimization problem. Simulations and actual star image restoration experiment are implemented to demonstrate that the centroids extraction accuracy of the proposed method is higher than 0.1 pixel, the running time is 3 to 5 times better than Richardson–Lucy filter or other methods based on image gradient constraint, and the peak signal to noise ratio (PSNR) of recovered star images excel results of several other image deconvolution methods. [ABSTRACT FROM AUTHOR]

This paper proposed a new method to improve the visibility and contrast of color images. The proposed method is a joint technique scheme of Color Adaptive Histogram Equalization (CAHE) and interpolation method. CAHE is an effective algorithm for image contrast enhancement, and interpolation method enhances the image by increasing the number of pixels without missing any intermediate component of image. Hence, the combination of both may give the good agreement of image visuality and contrast. [ABSTRACT FROM AUTHOR]

This paper presents a novel single image super-resolution (SR) reconstruction method using shifted kernel regression. Assuming that a low-resolution (LR) image is made by the shifted regression-based image degradation model, the proposed SR process shifts pixels in a regularly sampled LR image with a subpixel precision according to local gradient. The optimum displacement of each pixel is estimated using the Gaussian filtered second derivatives. The shifted low-resolution image is finally up-scaled by estimating the regression coefficients of the shifted kernels in the spatially adaptive manner. Experimental results show that the proposed method overcomes the limitation of existing intensity-based SR algorithms. The proposed SR algorithm can successfully restore sharp, clear edges without undesired artifacts such as ringing, inverted gradient, halo effects, etc. Without using an iterative process, the proposed single image SR algorithm can easily be implemented in the form of either pre- or post-processing filters for further enhancing the SR result of existing methods. [ABSTRACT FROM AUTHOR]

Multi-frame image super-resolution (SR) aims to utilize information from a set of low resolution (LR) images to compose a high-resolution (HR) one. As it is desirable or essential in many real applications, recent years have witnessed the growing interest in the problem of multi-frame SR reconstruction. In this paper, we propose a novel multi-frame image super-resolution algorithm built on the regularization framework. The objective functional to be minimized for the regularization framework consists of a fidelity term and a regularization term. A new fidelity term is formed by combining L 1 norm and L 2 norm according to defined Residual Weight Parameters (RWP) and Channel Weight Parameters (CWP). A new regularization term which adopts Regional Adaptive Weight Coefficients (RAWC) is proposed to keep edge and flat regions, which are implicitly described in LR images, sharp and smooth, respectively. Thorough experimental results show the new algorithm using both fidelity and regularization terms for SR reconstruction is effective than other methods. [ABSTRACT FROM AUTHOR]

Non-local means (NLM) filtering is an efficacious algorithm in image denoising which searches the similar neighborhoods and estimates the pixel by averaging these neighborhoods. Some internal parameters such as patch size, search window size and smoothing strength have serious effects on filtering performance. This paper proposes an improved version of NLM by using weak textured patches based single image noise estimation and two-stage NLM with adaptive smoothing parameter. Our proposed method firstly applies weak textured patches based noise estimation to achieve the noise level of input noisy image. Then relying on the estimated noise level, we apply the first stage NLM with adaptive smoothing parameter to attain a basic denoised image. After that, the basic denoised image is refined by the second stage of NLM with smaller smoothing strength. Our experimental results show that the proposed algorithm outperforms the NLM and some NLM recent variants both in visual quality and numerical measures. Additionally, the potential halo effect is almost eliminated in the result images produced by our proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes an image segmentation method for blurred trace infrared images, exploring statistical properties of blurred infrared data to segment target regions. I consider the function of immune factors for blurred infrared image segmentation, combined to the template algorithm framework. First, all of the pixel antigens are divided into three classes by innate recognition factors: target antigens, background antigens and blurred antigens. Next, the innate presentation factors present template characteristics as new features for each antigen. Finally, target and background antigens are used for generating mature adaptive immune factors, and these mature factors will recognise each blurred antigen into two classes: a target pixel or a background pixel. Experimental results indicate that the superstring galaxy template algorithm can improve the target segmentation rate and reduce the segmentation error rate. [ABSTRACT FROM AUTHOR]

Superpixel is one of the most popular image over-segmentations with broad applications in the computer vision field to reduce their computations by replacing pixels as primitives. The main concerns of one superpixel generation algorithm are its accuracy and efficiency. One of the most important things in superpixel accuracy is to fit the image boundaries tightly with a few pixels as possible (namely minimal contour density, which is measured by the percent of superpixel contour pixels in the whole image). In this paper, we propose a new fast algorithm based on the clustering method to produce superpixels accurately with low contour density. First, we adopt the linear path from a pixel to one superpixel seed to define a regular term and propose a new distance measurement between them. In addition, we introduce the gradient and Local Binary Pattern (LBP) features and propose formulas of parameters in the proposed method adaptively. In this way, we can use the new distance measurement to group pixels as initial regions adaptively and produce the final superpixels by merging those small ones. Finally, we test the new algorithm on two public datasets and compare it with the state-of-the-art. Our method can generate superpixels with lower contour density while being competitive in accuracy and computational time. [ABSTRACT FROM AUTHOR]

Image segmentation process is one of the most interesting and challenging problems in digital image processing tasks. The segmentation process involves finding similar regions within an image. Many segmentation problems are achieved by the incorporation of clustering techniques. One of the most common technique for clustering process is the Fuzzy C-means (FCM) algorithm. However, even when FCM is one of the most popular techniques applied in image segmentation, it presents some issues such as large computational time complexity, noise sensitivity, and initial cluster centers dependency. In order to solve these problems, this paper presents a Histogram Based Fuzzy Clustering (HBFC) technique using an improved version of Firefly Algorithm (FA). In the proposed approach, the FA involves three search strategies: rough set-based population, random attraction and local search mechanism. Also, the clustering process is conducted based on gray level histograms instead of single pixels of an image. Under such circumstances, the occurrence of misclassification of pixels is reduced. A rigorous comparative study is conducted among the proposed approach and several state-of-art Nature-Inspired Optimization Algorithms (NIOAs) and traditional clustering techniques. The numerical results indicate that the proposed approach outperform the well-known NIOA based clustering methods in terms of precision, robustness and quality of the segmented outputs. • An improved Firefly algorithm has been proposed for Image Clustering. • Random Attraction model and rough set have been utilized. • The proposed approach is a histogram based fast fuzzy clustering model. • The proposed method is compared against state-of-art segmentation methods. • Numerical results indicate the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

• Appropriate local blur scales are selected when image block is reblurred. • Sub-band fusion of DCT coefficients is proposed in calculating blur amount. • Edge blur maps are computed in multi-scale and multi-orientation image windows. • Blur points are added to initialize input image in image matting stage. In common natural image blur, objects that not lie in the focal length of a digital camera generate defocus areas in the photographed image. In this paper, we propose a novel edge-based method for spatially varying defocus blur detection based on reblurred DCT coefficients ratios of the corresponding local patches. This method selects appropriate local reblur scales while detecting the edge points to deal with the problem of different blur degree and texture richness of the local image blocks. A sub-band fusion method of DCT coefficients is proposed to expand the difference between DCT features of in-focus and out-of-focus regions. Edge points blur maps are computed in multi-scale and multi-orientation image windows and more blur points are added to initialize sparse blur maps, finally Matting Laplacian method is used along with multi-scale fusion algorithm to obtain a more accurate blur segmentation. Experimental results present the proposed method has strong advantages in image detail processing and outperforms state-of-the-art methods for blur detection. [ABSTRACT FROM AUTHOR]

This paper proposes an automatic crack detection, localization, and quantification method using the integration of a faster region proposal convolutional neural network (Faster R-CNN) algorithm to detect crack regions. The regions were located using various bounding boxes and a modified tubularity flow field (TuFF) algorithm to segment the crack pixels from the detected crack regions. A modified distance transform method (DTM) was used to measure crack thickness and length in terms of pixel measurement. To validate the proposed method, 100 images were taken in different places with complex backgrounds containing different angles and distances between the camera and the objects. The results obtained from the Faster-R-CNN-based crack damage detection had a 95% average precision. The pixel-level segmentation performance of the modified TuFF algorithm exhibited an authentic outcome, with 83% intersection over union. Finally, the modified DTM algorithm provided 93% accuracy with respect to crack length and thickness with a 2.6 pixel root mean square error. • The first proposal of an automatic hybrid crack segmentation and quantification under complex backgrounds and different lightening conditions. • The hybrid method successfully removes the limitation of independent application of deep learning and image processing method. • The proposed method achieved the AP of the Faster R-CNN is 95%, and the IoU of the modified TuFF with CLAHE is 83%. • The original DTM method is improved to measure the thickness and length of the segmented cracks with an RMS error of 2.6 pixels, providing crack length accuracy of 93%. [ABSTRACT FROM AUTHOR]