116 research outputs found
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
Joint Motion Deblurring and Superresolution from Single Blurry Image
Currently superresolution from a motion blurred image still remains a challenging task. The conventional approach, which preprocesses the blurry low resolution (LR) image with a deblurring algorithm and employs a superresolution algorithm, has the following limitation. The high frequency texture of the image is unavoidably lost in the deblurring process and this loss restricts the performance of the subsequent superresolution process. This paper presents a novel technique that performs motion deblurring and superresolution jointly from one single blurry image. The basic idea is to regularize the ill-posed reconstruction problem using an edge-preserving gradient prior and a sparse kernel prior. This method derives from an inverse problem approach under an efficient optimization scheme that alternates between blur kernel estimation and superresolving until convergence. Furthermore, this paper proposes a simple and efficient refinement formulation to remove artifacts and render better deblurred high resolution (HR) images. The improvements brought by the proposed combined framework are demonstrated by the processing results of both simulated and real-life images. Quantitative and qualitative results on challenging examples show that the proposed method outperforms the existing state-of-the-art methods and effectively eliminates motion blur and artifacts in the superresolved image
Semi-Blind Spatially-Variant Deconvolution in Optical Microscopy with Local Point Spread Function Estimation By Use Of Convolutional Neural Networks
We present a semi-blind, spatially-variant deconvolution technique aimed at
optical microscopy that combines a local estimation step of the point spread
function (PSF) and deconvolution using a spatially variant, regularized
Richardson-Lucy algorithm. To find the local PSF map in a computationally
tractable way, we train a convolutional neural network to perform regression of
an optical parametric model on synthetically blurred image patches. We
deconvolved both synthetic and experimentally-acquired data, and achieved an
improvement of image SNR of 1.00 dB on average, compared to other deconvolution
algorithms.Comment: 2018/02/11: submitted to IEEE ICIP 2018 - 2018/05/04: accepted to
IEEE ICIP 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
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