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Texture Enhanced Image Denoising via Gradient Histogram Preservation
"... Image denoising is a classical yet fundamental problem in low level vision, as well as an ideal test bed to evaluate various statistical image modeling methods. One of the most challenging problems in image denoising is how to preserve the fine scale texture structures while removing noise. Various ..."
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Image denoising is a classical yet fundamental problem in low level vision, as well as an ideal test bed to evaluate various statistical image modeling methods. One of the most challenging problems in image denoising is how to preserve the fine scale texture structures while removing noise. Various natural image priors, such as gradient based prior, nonlocal self-similarity prior, and sparsity prior, have been extensively exploited for noise removal. The denoising algorithms based on these priors, however, tend to smooth the detailed image textures, degrading the image visual quality. To address this problem, in this paper we propose a texture enhanced image denoising (TEID) method by enforcing the gradient distribution of the denoised image to be close to the estimated gradient distribution of the original image. A novel gradient histogram preservation (GHP) algorithm is developed to enhance the texture structures while removing noise. Our experimental results demonstrate that the proposed GHP based TEID can well preserve the texture features of the denoised images, making them look more natural. 1.
Unified Blind Method for Multi-Image Super-Resolution and Single/Multi-Image Blur Deconvolution
"... Abstract — This paper presents, for the first time, a unified blind method for multi-image super-resolution (MISR or SR), single-image blur deconvolution (SIBD), and multi-image blur deconvolution (MIBD) of low-resolution (LR) images degraded by linear space-invariant (LSI) blur, aliasing, and addit ..."
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Abstract — This paper presents, for the first time, a unified blind method for multi-image super-resolution (MISR or SR), single-image blur deconvolution (SIBD), and multi-image blur deconvolution (MIBD) of low-resolution (LR) images degraded by linear space-invariant (LSI) blur, aliasing, and additive white Gaussian noise (AWGN). The proposed approach is based on alternating minimization (AM) of a new cost function with respect to the unknown high-resolution (HR) image and blurs. The regularization term for the HR image is based upon the Huber-Markov random field (HMRF) model, which is a type of variational integral that exploits the piecewise smooth nature of the HR image. The blur estimation process is supported by an edge-emphasizing smoothing operation, which improves the quality of blur estimates by enhancing strong soft edges toward step edges, while filtering out weak structures. The parameters are updated gradually so that the number of salient edges used for blur estimation increases at each iteration. For better performance, the blur estimation is done in the filter domain rather than the pixel domain, i.e., using the gradients of the LR and HR images. The regularization term for the blur is Gaussian (L2 norm), which allows for fast noniterative optimization in the frequency domain. We accelerate the processing time of SR reconstruction by separating the upsampling and registration processes from the optimization procedure. Simulation results on both synthetic and real-life images (from a novel computational imager) confirm the robustness and effectiveness of the proposed method. Index Terms — Blur deconvolution, blind estimation, Huber-Markov Random Field (HMRF) prior, image restoration, superresolution.
Towards Motion-Aware Light Field Video for Dynamic Scenes
"... Current Light Field (LF) cameras offer fixed resolution in space, time and angle which is decided a-priori and is independent of the scene. These cameras either trade-off spatial resolution to capture single-shot LF [20, 27, 12] or tradeoff temporal resolution by assuming a static scene to capture h ..."
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Current Light Field (LF) cameras offer fixed resolution in space, time and angle which is decided a-priori and is independent of the scene. These cameras either trade-off spatial resolution to capture single-shot LF [20, 27, 12] or tradeoff temporal resolution by assuming a static scene to capture high spatial resolution LF [18, 3]. Thus, captur-ing high spatial resolution LF video for dynamic scenes re-mains an open and challenging problem. We present the concept, design and implementation of a LF video camera that allows capturing high resolution LF video. The spatial, angular and temporal resolution are not fixed a-priori and we exploit the scene-specific re-dundancy in space, time and angle. Our reconstruction is motion-aware and offers a continuum of resolution trade-off with increasing motion in the scene. The key idea is (a) to design efficient multiplexing matrices that allow resolu-tion tradeoffs, (b) use dictionary learning and sparse repre-sentations for robust reconstruction, and (c) perform local motion-aware adaptive reconstruction. We perform extensive analysis and characterize the per-formance of our motion-aware reconstruction algorithm. We show realistic simulations using a graphics simulator as well as real results using a LCoS based programmable camera. We demonstrate novel results such as high resolu-tion digital refocusing for dynamic moving objects. 1.
Image deblurring with low-rank approximation structured sparse representation
- In Signal & Information Processing Association Annual Summit and Conference (APSIPA ASC), Asia-Pacific
, 2012
"... Abstract — In recent years sparse representation model (SRM) based image deblurring approaches have shown promising image deblurring results. However, since most of the current SRMs don’t utilize the spatial correlations between the nonzero sparse coefficients, the SRM-based image deblurring methods ..."
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Abstract — In recent years sparse representation model (SRM) based image deblurring approaches have shown promising image deblurring results. However, since most of the current SRMs don’t utilize the spatial correlations between the nonzero sparse coefficients, the SRM-based image deblurring methods often fail to faithfully recover sharp image edges. In this paper, a structured SRM is employed to exploit the local and nonlocal spatial correlation between the sparse codes. The connection between the structured SRM and the low-rank approximation model has also been exploited. An effective image deblurring algorithm using the patch-based structured SRM is then proposed. Experimental results demonstrate the improvements of the proposed deblurring method over current state-of-the-art image deblurring methods. I.
A FAST PATCH-DICTIONARY METHOD FOR WHOLE IMAGE RECOVERY
"... Abstract. Various algorithms have been proposed for dictionary learning. Among those for image processing, many use image patches to form dictionaries. This paper focuses on whole-image recovery from corrupted linear measurements. We address the open issue with representing an image by overlapping p ..."
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Abstract. Various algorithms have been proposed for dictionary learning. Among those for image processing, many use image patches to form dictionaries. This paper focuses on whole-image recovery from corrupted linear measurements. We address the open issue with representing an image by overlapping patches: the overlapping leads to an excessive number of dictionary coefficients to determine. With very few exceptions, this issue has limited the applications of image-patch methods to the “local ” kind of tasks such as denoising, inpainting, cartoon-texture decomposition, super-resolution, and image deblurring, for which one can process a few patches at a time. Our focus is global imaging tasks such as compressive sensing and medical image recovery, where the whole image is encoded together, making it either impossible or very ineffective to update a few patches at a time. Our strategy is to divide the sparse recovery into multiple subproblems, each of which handles a subset of non-overlapping patches, and then the results of the subproblems are averaged to yield the final recovery. This simple strategy is surprisingly effective in terms of both quality and speed. In addition, we accelerate computation of dictionary learning by applying a recent block proximal-gradient method, which not only has a lower per-iteration complexity but also takes fewer iterations to converge, compared to the current state-of-the-art. We also establish that our algorithm globally converges to a stationary point. Numerical results on synthetic data demonstrate that our algorithm can recover a more faithful dictionary than two state-of-the-art methods. Combining our whole-image recovery and dictionary-learning methods, we numerically simulate image inpainting, compres-sive sensing recovery, and deblurring. Our recovery is more faithful than those out of a total variation method and a method based on overlapping patches. Our matlab code is competitive in terms of both speed and quality. Key words. whole-image recovery, dictionary learning, block proximal gradient method, sparse optimization 1. Introduction. Our
1Mixed Noise Removal by Weighted Encoding with Sparse Nonlocal Regularization
"... Abstract—Mixed noise removal from natural images is a challenging task since the noise distribution usually does not have a parametric model and has a heavy tail. One typical kind of mixed noise is additive white Gaussian noise (AWGN) coupled with impulse noise (IN). Many mixed noise removal methods ..."
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Abstract—Mixed noise removal from natural images is a challenging task since the noise distribution usually does not have a parametric model and has a heavy tail. One typical kind of mixed noise is additive white Gaussian noise (AWGN) coupled with impulse noise (IN). Many mixed noise removal methods are detection based methods. They first detect the locations of impulse noise pixels and then remove the mixed noise. However, such methods tend to generate many artifacts when the mixed noise is strong. In this paper, we propose a simple yet effective method, namely weighted encoding with sparse nonlocal regularization (WESNR), for mixed noise removal. In WESNR, there is not an explicit step of impulse pixel detection; instead, soft impulse pixel detection via weighted encoding is used to deal with IN and AWGN simultaneously. Meanwhile, the image sparsity prior and nonlocal self-similarity prior are integrated into a regularization term and introduced into the variational encoding framework. Experimental results show that the proposed WESNR method achieves leading mixed noise removal performance in terms of both quantitative measures and visual quality. Index Terms—Mixed noise removal, weighted encoding, non-local, sparse representation. I.
Gradient Histogram Estimation and Preservation for Texture Enhanced Image Denoising
, 2013
"... Natural image statistics plays an important role in image denoising, and various natural image priors, including gradient based, sparse representation based and nonlocal selfsimilarity based ones, have been widely studied and exploited for noise removal. In spite of the great success of many denois ..."
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Natural image statistics plays an important role in image denoising, and various natural image priors, including gradient based, sparse representation based and nonlocal selfsimilarity based ones, have been widely studied and exploited for noise removal. In spite of the great success of many denoising algorithms, they tend to smooth the fine scale image textures when removing noise, degrading the image visual quality. To address this problem, in this paper we propose a texture enhanced image denoising method by enforcing the gradient histogram of the denoised image to be close to a reference gradient histogram of the original image. Given the reference gradient histogram, a novel gradient histogram preservation (GHP) algorithm is developed to enhance the texture structures while removing noise. Two region-based variants of GHP are proposed for the denoising of images consisting of regions with different textures. An algorithm is also developed to effectively estimate the reference gradient histogram from the noisy observation of the unknown image. Our experimental results demonstrate that the proposed GHP algorithm can well preserve the texture appearance in the denoised images, making them look more natural.
Sparse Representation based Image Interpolation with Nonlocal Autoregressive Modeling
"... Sparse representation has proven to be a promising approach to image super-resolution, where the low resolution (LR) image is usually modeled as the down-sampled version of its high resolution (HR) counterpart after blurring. When the blurring kernel is the Dirac delta function, i.e., the LR image ..."
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Sparse representation has proven to be a promising approach to image super-resolution, where the low resolution (LR) image is usually modeled as the down-sampled version of its high resolution (HR) counterpart after blurring. When the blurring kernel is the Dirac delta function, i.e., the LR image is directly down-sampled from its HR counterpart without blurring, the super-resolution problem becomes an image interpolation problem. In such case, however, the conventional sparse representation models (SRM) become less effective because the data fidelity term will fail to constrain the image local structures. In natural images, fortunately, the many nonlocal similar patches to a given patch could provide nonlocal constraint to the local structure. In this paper we incorporate the image nonlocal self-similarity into SRM for image interpolation. More specifically, a nonlocal autoregressive model (NARM) is proposed and taken as the data fidelity term in SRM. We show that the NARM induced sampling matrix is less coherent with the representation dictionary, and consequently makes SRM more effective for image interpolation. Our extensive experimental results demonstrated that the proposed NARM based image interpolation method can effectively reconstruct the edge structures and suppress the jaggy/ringing artifacts, achieving the best image interpolation results so far in term of PSNR as well as perceptual quality metrics such as SSIM and FSIM.
Is Kinect Depth Data Accurate for the Aesthetic Evaluation after Breast Cancer Surgeries
- in Proceedings of the 6th Iberian Conference on Pattern Recognition and Image Analysis
, 2013
"... Abstract. The conservative treatment is now the preferred procedure to treat breast cancer mainly due to better aesthetical results obtained. However, the aesthetic outcome is diverse and very difficult to evaluate, which motivates the research on automatic methodologies. The use of three-dimensiona ..."
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Abstract. The conservative treatment is now the preferred procedure to treat breast cancer mainly due to better aesthetical results obtained. However, the aesthetic outcome is diverse and very difficult to evaluate, which motivates the research on automatic methodologies. The use of three-dimensional (3D) methodologies is increasing; however, the high cost of the equipment and the need for specialised technicians to oper-ate it are import setbacks. Consequently, the search for affordable and easy to perform equipments is highly desirable. This paper studies the application of a Kinect device in this field, addressing issues related to accuracy, resolution and quality of the data. The paper demonstrates a comparative study of state-of-the-art Super-Resolution (SR) algorithms applied to the Kinect depth data, and the importance to improve the quality of images is stressed. The results demonstrate that it is possible to measure volumetric information and that there is agreement between features and the subjective aesthetic evaluation.
1Pattern Masking Estimation in Image with Structural Uncertainty
"... Abstract—A model of visual masking, which reveals the vis-ibility of stimuli in the human visual system (HVS), is useful in perceptual based image/video processing. The existing visual masking function mainly takes luminance contrast into account, which always overestimates the visibility threshold ..."
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Abstract—A model of visual masking, which reveals the vis-ibility of stimuli in the human visual system (HVS), is useful in perceptual based image/video processing. The existing visual masking function mainly takes luminance contrast into account, which always overestimates the visibility threshold of the edge region and underestimates that of the texture region. Recent re-search on visual perception indicates that the HVS is sensitive to orderly regions which possess regular structures, and insensitive to disorderly regions which possess uncertain structures. There-fore, structural uncertainty is another determining factor on visual masking. In this paper, we introduce a novel pattern mask-ing function based on both luminance contrast and structural uncertainty. By mimicking the internal generative mechanism of the HVS, a prediction model is firstly employed to separate out the unpredictable uncertainty from an input image. And then, an improved local binary pattern is introduced to compute the structural uncertainty. Finally, combining luminance contrast with structural uncertainty, the pattern masking function is deduced. Experimental result demonstrates that the proposed pattern masking function outperforms the existing visual masking function. Furthermore, we extend the pattern masking function to just noticeable difference (JND) estimation and introduce a novel pixel domain JND model. Subjective viewing test confirms that the proposed JND model is more consistent with the HVS than the existing JND models.
