Localized and computationally efficient approach to shift-variant image deblurring (original) (raw)
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Adaptive-neighborhood image deblurring
Journal of Electronic Imaging, 1994
This paper presents a new technique for the restoration of images degraded by a linear, shift-invariant blurring point-spread function (PSF) in the presence of additive white Gaussian noise. The algorithm uses overlapping variable-size, variable-shape adaptiveneighborhoods (ANs) to de ne stationary regions in the input image and obtains a spectral estimate of the noise in each AN region. This estimate is then used to obtain a spectral estimate of the original undegraded AN region, which is inverse Fourier transformed to obtain the space-domain deblurred AN region. The regions are then combined to form the nal restored image. Mathematical derivation and implementation of the adaptive-neighborhood deblurring (AND) lter will be discussed, and experimental results will be presented with an analysis of the performance of the AND lter as compared to the xed-neighborhood sectioned deblurring (FNSD) Wiener and power spectrum equalization (PSE) lters. It will be shown that using the AND algorithm for image deblurring will enable the identi cation of relatively stationary regions. This improves the restoration process and produces results that are superior to those obtained using the FNSD method both visually and in terms of quantitative error measures.
2016
Degradations of images during the acquisition process is inevitable; images suffer from blur and noise. With advances in technologies and computational tools, the degradations in the images can be avoided or corrected up to a significant level, however, the quality of acquired images is still not adequate for many applications. This calls for the development of more sophisticated digital image restoration tools. This thesis is a contribution to image restoration. The thesis is divided into five chapters, each including a detailed discussion on different aspects of image restoration. It starts with a generic overview of imaging systems, and points out the possible degradations occurring in images with their fundamental causes. In some cases the blur can be considered stationary throughout the field-of-view, and then it can be simply modeled as convolution. However, in many practical cases, the blur varies throughout the field-of-view, and thus modeling the blur is not simple consider...
A Comparative Study On Image Deblurring Techniques
2014
Image blur is a common problem that occurs when recording digital images due to camera shake, long exposure time, or movement of objects. As a result, the recorded image is degraded and the recorded scene becomes unreadable. Recently, the field of blur removal has gained increasing interest in a lot of researches. The problem is known as blind deconvolution if the only available information is the blurred image and there is no knowledge about the blurring model or the Point Spread Function (PSF). In this case, the basic target of the process is to recover both the blur kernel and the deblurred (latent) image, simultaneously. In this paper, we introduced a comprehensive study on the image deblurring, type of blur, noise model and finally a comparative study of different image deblurring techniques. We performed several experiments to evaluate these techniques in terms of performance, blur type, Peak Signal to Noise Ratio and structural similarity (SSIM).
Distributed approach for deblurring large images with shift-variant blur
2017 25th European Signal Processing Conference (EUSIPCO), 2017
Image deblurring techniques are effective tools to obtain high quality image from acquired image degraded by blur and noise. In applications such as astronomy and satellite imaging, size of acquired images can be extremely large (up to gigapixels) covering a wide field-of-view suffering from shiftvariant blur. Most of the existing deblurring techniques are designed to be cost effective on a centralized computing system having a shared memory and possibly multicore processor. The largest image they can handle is then conditioned by the memory capacity of the system. In this paper, we propose a distributed shift-variant image deblurring algorithm in which several connected processing units (each with reasonable computational resources) can deblur simultaneously different portions of a large image while maintaining a certain coherency among them to finally obtain a single crisp image. The proposed algorithm is based on a distributed Douglas-Rachford splitting algorithm with a specific structure of the penalty parameters used in the proximity operator. Numerical experiments show that the proposed algorithm produces images of similar quality as the existing centralized techniques while being distributed and being cost effective for extremely large images.
Efficient, blind, spatially-variant deblurring for shaken images
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In this chapter we discuss modeling and removing spatially-variant blur from photographs. We describe a compact global parameterization of camera shake blur, based on the 3D rotation of the camera during the exposure. Our model uses three-parameter homographies to connect camera motion to image motion and, by assigning weights to a set of these homographies, can be seen as a generalization of the standard, spatially-invariant convolutional model of image blur. As such we show how existing algorithms, designed for spatially-invariant deblurring, can be "upgraded" in a straightforward manner to handle spatially-variant blur instead. We demonstrate this with algorithms working on real images, showing results for blind estimation of blur parameters from single images, followed by non-blind image restoration using these parameters. Finally, we introduce an efficient approximation to the global model, which significantly reduces the computational cost of modeling the spatially-variant blur. By approximating the blur as locally-uniform, we can take advantage of fast Fourier-domain convolution and deconvolution, reducing the time required for blind deblurring by an order of magnitude.
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2007
We propose and test a simple algorithmic framework for recovering images from blurry and noisy observations based on total variation (TV) regularization when a blurring point-spread function is given. Using a splitting technique, we construct an iterative procedure of alternately solving a pair of easy subproblems associated with an increasing sequence of penalty parameter values. The main computation at each iteration is three Fast Fourier Transforms (FFTs).
Deblurring a Camera-Shake image using a Thinning Kernel
Proceedings of The 6th IIAE International Conference on Intelligent Systems and Image Processing 2018, 2018
The task of blind deblurring usually consists of estimation of interim images and blur kernels. Due to the lack of information in kernels compared to that in interim images, when only a blurred image is available, most of deblurring methods emphasis the estimation of interim images. However, the resulting kernel is often wider than it should be, thus degrading the quality of the deconvolved image. To remedy the problem of wide kernels, we present a thinning scheme to better estimate a kernel. In this way, a clear image can be recovered from a camera-shake blurred image. To mitigate the insufficient information of blur kernels, we make simple inferences and assumptions for kernels based on the trajectory of the camera shake. Under these inferences and assumptions, we use a three-step approach to estimate the blur kernel. Firstly, we relax the condition to find the shape of the blur kernel. Next, we use a thinning algorithm to obtain the skeleton of the blur kernel. Thirdly, we reweight the blur kernel by Gaussian distribution. By repeating these steps a few times we can get a more accurate blur kernel. Finally, we can reconstruct a high quality deblurred image by using the blur kernel. The proposed method is tested by a public database and our results outperform those of two similar methods.
Blind Image Deblurring based on Kernel Mixture. (arXiv:2101.06241v1 [cs.CV])
arXiv (Cornell University), 2021
Blind Image deblurring tries to estimate blurriness and a latent image out of a blurred image. This estimation, as being an ill-posed problem, requires imposing restrictions on the latent image or a blur kernel that represents blurriness. Different from recent studies that impose some priors on the latent image, this paper regulates the structure of the blur kernel. We propose a kernel mixture structure while using the Gaussian kernel as a base kernel. By combining multiple Gaussian kernels structurally enhanced in terms of scales and centers, the kernel mixture becomes capable of modeling nearly non-parametric shape of blurriness. A data-driven decision for the number of base kernels to combine makes the structure even more flexible. We apply this approach to a remote sensing problem to recover images from blurry images of satellite. This case study shows the superiority of the proposed method regulating the blur kernel in comparison with state-of-the-art methods that regulates the latent image.
Disparity-Based Space-Variant Image Deblurring
2013
Obtaining a good-quality image requires exposure to light for an appropriate amount of time. If there is camera or object motion during the exposure time, the image is blurred. To remove the blur, some recent image deblurring methods effectively estimate a point spread function (PSF) by acquiring a noisy image additionally, and restore a clear latent image with the PSF. Since the groundtruth PSF varies with the location, a blockwise approach for PSF estimation has been proposed. However, the block to estimate a PSF is a straightly demarcated rectangle which is generally different from the shape of an actual region where the PSF can be properly assumed constant. We utilize the fact that a PSF is substantially related to the local disparity between two views. This paper presents a disparity-based method of space-variant image deblurring which employs disparity information in image segmentation, and estimates a PSF, and restores a latent image for each region. The segmentation method firstly over-segments a blurred image into sufficiently many regions based on color, and then merges adjacent regions with similar disparities. Experimental results show the effectiveness of the proposed method.
A fast and robust deblurring technique on high noise environment
2013 IEEE International Conference on Image Processing, 2013
To have a unique solution of an ill-posed inverse problem, the usual way is to embed prior information in terms of regularizer or smoothness criterion. In this work, both the inverse mechanism (the relationship of blur and sharp patches) and the smoothness prior are learned simultaneously from the image itself, in multiple scales. We have shown experimentally that the proposed method outperform the existing state-of-the-art techniques on high noise environment and produce comparable result otherwise; moreover, it is almost three times faster than existing ones.