Open AccessJournal Article
A Review on Estimation of Defocus Blur from a Single Image
TLDR
Some of the methods like local contrast prior, defocus magnification and spectrum contrast for estimating the defocus blur map are discussed.Abstract:
Defocus estimation is an important part in high quality image processing field, which mainly includes edge detection, image deblurring, and measuring image quality. Wrong lens setting or shallow depth of field which would produce defocus blur. Defocus blur is most of the times present in natural images. In this paper we have discussed some of the methods like local contrast prior, defocus magnification and spectrum contrast for estimating the defocus blur map.read more
Citations
More filters
Journal ArticleDOI
Automatic Extraction of Blur Regions on a Single Image Based on Semantic Segmentation
TL;DR: A blur region detection method based on semantic segmentation is proposed to extract blur regions, which well integrates global image-level context and cross-layer context information making the auto-learned features more robust.
Book ChapterDOI
Blind Space-Variant Single-Image Restoration of Defocus Blur
TL;DR: This work addresses the problem of blind piecewise space-variant image deblurring where only part of the image is sharp, assuming a shallow depth of field which imposes significant defocus blur.
References
More filters
Journal ArticleDOI
Removing camera shake from a single photograph
TL;DR: This work introduces a method to remove the effects of camera shake from seriously blurred images, which assumes a uniform camera blur over the image and negligible in-plane camera rotation.
Proceedings ArticleDOI
Understanding and evaluating blind deconvolution algorithms
TL;DR: The previously reported failure of the naive MAP approach is explained by demonstrating that it mostly favors no-blur explanations and it is shown that since the kernel size is often smaller than the image size a MAP estimation of the kernel alone can be well constrained and accurately recover the true blur.
Proceedings ArticleDOI
Analyzing spatially-varying blur
TL;DR: Two contributions are made: a local blur cue that measures the likelihood of a small neighborhood being blurred by a candidate blur kernel; and an algorithm that, given an image, simultaneously selects a motion blur kernel and segments the region that it affects.
Journal ArticleDOI
Estimating Spatially Varying Defocus Blur From A Single Image
TL;DR: This method is capable of measuring the probability of local defocus scale in the continuous domain and takes smoothness and color edge information into consideration to generate a coherent blur map indicating the amount of blur at each pixel.
Proceedings ArticleDOI
Single image defocus map estimation using local contrast prior
Yu-Wing Tai,Michael S. Brown +1 more
TL;DR: This paper presents a simple yet effective approach for estimating a defocus blur map based on the relationship of the contrast to the image gradient in a local image region, called the local contrast prior.