CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Improving Image Restoration with Soft-Rounding
Mei, Xing1,3; Qi, Honggang2; Hu, Bao-Gang3; Lyu, Siwei1
Conference NameInternational Conference on Computer Vision (ICCV)
Conference Date2015
Conference PlaceSantiago, Chile
Several important classes of images such as text, barcode and pattern images have the property that pixels can only take a distinct subset of values. This knowledge can benefit
the restoration of such images, but it has not been widely considered in current restoration methods. In this work, we describe an effective and efficient approach to incorporate
the knowledge of distinct pixel values of the pristine images into the general regularized least squares restoration frame-work. We introduce a new regularizer that attains zero at the designated pixel values and becomes a quadratic penalty function in the intervals between them. When incorporated into the regularized least squares restoration framework, this regularizer leads to a simple and efficient step that re-
sembles and extends the rounding operation, which we term as soft-rounding. We apply the soft-rounding enhanced solution to the restoration of binary text/barcode images and
pattern images with multiple distinct pixel values. Experimental results show that soft-rounding enhanced restoration methods achieve significant improvement in both visual
quality and quantitative measures (PSNR and SSIM). Furthermore, we show that this regularizer can also benefit the restoration of general natural images.
Indexed ByEI
Document Type会议论文
Affiliation1.Computer Science Department, University at Albany, SUNY
2.Computer Science Department, University of Chinese Academy of Sciences
3.NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Recommended Citation
GB/T 7714
Mei, Xing,Qi, Honggang,Hu, Bao-Gang,et al. Improving Image Restoration with Soft-Rounding[C],2015.
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