A computational photography algorithm for quality enhancement of single lens imaging deblurring

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• 作者：Weili Li^a ; ^{liweili6563@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Yu Liu^a ; ^{jasonyuliu@nudt.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Xiaoqing Yin^b ; ^{yinxiaoqing89@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Bin Wang^a ; ^{nudtwangbing@163.com" class="auth_mail" title="E-mail the corresponding author} ; Maojun Zhang^a ; ^{zmjbar@163.com" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Single lens imaging ; Point spread function ; Deconvolution ; Optimization
• 刊名：Optik - International Journal for Light and Electron Optics
• 出版年：2015
• 出版时间：November 2015
• 年：2015
• 卷：126
• 期：21
• 页码：2788-2792
• 全文大小：1121 K

文摘

Modern single lens reflex lenses typically consist of up to two dozen individual optical elements, the complexity of which is necessary to compensate for geometric and chromatic aberrations. This paper adopts one single lens to capture images instead of complex lenses, and computational photography technique is employed to remove corresponding imaging artifacts. We initially estimate the space-variant point spread function of the single lens by combining l₁/l₂ image and sparse kernel priors. A fast non-blind deconvolution method with hyper-Laplacian prior is then performed to recover clear image. Experimental results show that the proposed method is at par with state-of-the-art non-blind deconvolution approaches, especially with regards its speed that is much faster than that of existing methods.