64 research outputs found
Correct spatially varying image blur by Projective Motion Richardson-Lucy Algorithm and Blur Image alignment
Master'sMASTER OF ENGINEERIN
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image
deblurring, including non-blind/blind, spatially invariant/variant deblurring
techniques. Indeed, these techniques share the same objective of inferring a
latent sharp image from one or several corresponding blurry images, while the
blind deblurring techniques are also required to derive an accurate blur
kernel. Considering the critical role of image restoration in modern imaging
systems to provide high-quality images under complex environments such as
motion, undesirable lighting conditions, and imperfect system components, image
deblurring has attracted growing attention in recent years. From the viewpoint
of how to handle the ill-posedness which is a crucial issue in deblurring
tasks, existing methods can be grouped into five categories: Bayesian inference
framework, variational methods, sparse representation-based methods,
homography-based modeling, and region-based methods. In spite of achieving a
certain level of development, image deblurring, especially the blind case, is
limited in its success by complex application conditions which make the blur
kernel hard to obtain and be spatially variant. We provide a holistic
understanding and deep insight into image deblurring in this review. An
analysis of the empirical evidence for representative methods, practical
issues, as well as a discussion of promising future directions are also
presented.Comment: 53 pages, 17 figure
Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal
In this paper, we address the problem of estimating and removing non-uniform
motion blur from a single blurry image. We propose a deep learning approach to
predicting the probabilistic distribution of motion blur at the patch level
using a convolutional neural network (CNN). We further extend the candidate set
of motion kernels predicted by the CNN using carefully designed image
rotations. A Markov random field model is then used to infer a dense
non-uniform motion blur field enforcing motion smoothness. Finally, motion blur
is removed by a non-uniform deblurring model using patch-level image prior.
Experimental evaluations show that our approach can effectively estimate and
remove complex non-uniform motion blur that is not handled well by previous
approaches.Comment: This is a final version accepted by CVPR 201
An Improved Adaptive Deconvolution Algorithm for Single Image Deblurring
One of the most common defects in digital photography is motion blur caused by camera shake. Shift-invariant motion blur can be modeled as a convolution of the true latent image and a point spread function (PSF) with additive noise. The goal of image deconvolution is to reconstruct a latent image from a degraded image. However, ringing is inevitable artifacts arising in the deconvolution stage. To suppress undesirable artifacts, regularization based methods have been proposed using natural image priors to overcome the ill-posedness of deconvolution problem. When the estimated PSF is erroneous to some extent or the PSF size is large, conventional regularization to reduce ringing would lead to loss of image details.
This paper focuses on the nonblind deconvolution by adaptive regularization which preserves image details, while suppressing ringing artifacts. The way is to control the regularization weight adaptively according to the image local characteristics. We adopt elaborated reference maps that indicate the edge strength so that textured and smooth regions can be distinguished. Then we impose an appropriate constraint on the optimization process. The experiments’ results on both synthesized and real images show that our method can restore latent image with much fewer ringing and favors the sharp edges
Hybrid Approach of Nu-Mob, Mobil and MOBILAP for Face Recognition System
This is the first attempt to systematically address face recognition under (i) non-uniform motion blur and (ii) the combined effects of blur, illumination and pose. In this paper, we propose a methodology for face recognition in the presence of space-varying motion blur comprising of kernels. We model the blurred face as a convex combination of geometrically transformed instances of the focused gallery face, and show that the set of all images obtained by non-uniformly blurring a given image forms a convex set. We first propose a non-uniform blur-robust algorithm by blurring the gallery image’s with its corresponding TSF function and extract LBP features and finally returns the identity of the probe image by comparing the LBP features of the probe image with those of the transformed gallery images and find the closest match. Then we propose the motion blur and illumination-robust algorithm by blurring and re-illuminating the gallery image’s with its corresponding optimal TSF function and illumination coefficients and extract LBP features and finally returns the identity of the probe image. Finally we propose the motion, blur, illumination and pose-robust algorithm by estimating and synthesizing the new pose of the blurred probe image and then blurring and re-illuminating the gallery image’s with its corresponding optimal TSF function and illumination coefficients and extract LBP features and finally finds the closest match of the given input probe image
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