Abstract
Sparse representation has been proven to be a very effective technique for various image restoration applications. In this paper, an improved sparse representation based method is proposed to detect and estimate defocus blur of imaging sensors. Considering the fact that the patterns usually vary remarkably across different images or different patches in a single image, it is unstable and time-consuming for sparse representation over an over-complete dictionary. We propose an adaptive domain selection scheme to prelearn a set of compact dictionaries and adaptively select the optimal dictionary to each image patch. Then, with nonlocal structure similarity, the proposed method learns nonzero-mean coefficients’ distributions that are much more closer to the real ones. More accurate sparse coefficients can be obtained and further improve the performance of results. Experimental results validate that the proposed method outperforms existing defocus blur estimation approaches, both qualitatively and quantitatively.
Highlights
Blur is an image degradation that commonly appears in consumer-level images obtained from a variety of image sensors [1,2,3,4]
The circle region is called the circle of confusion (CoC) that results in defocus blur
The blurry regions in all tested images are masked out as ground-truth, which indicates the clear regions with respect to the defocus blur regions
Summary
Blur is an image degradation that commonly appears in consumer-level images obtained from a variety of image sensors [1,2,3,4]. For scenes with multiple depth layers, only the layer on a focal plane will focus on the camera sensor, which leads to others being out of focus. This phenomenon may sometimes strengthen a photo’s expressiveness, while, in most cases, it will lead to loss of texture details or incomprehensible information. The circle region is called the circle of confusion (CoC) that results in defocus blur.
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