自动化学报2017，Vol.43Issue(5)：765-777,13.DOI:10.16383/j.aas.2017.c160268
基于自适应块组割先验的噪声图像超分辨率重建
Noisy Image Super-resolution Reconstruction with Adaptive Patch-group-cuts Prior
摘要
Abstract
To enhance resolution of a noisy image, the conventional method adopts a cascaded scheme of denoising followed by super-resolution (SR) reconstruction. However, the denoising algorithm inevitably causes some loss of high-frequency information in the image, especially in non-smooth regions, which significantly influences the quality of the subsequent SR reconstruction. To incorporate all the high-frequency information from the noisy low-resolution (LR) images into the SR reconstruction, a noisy image SR method with adaptive patch-group-cuts (PGCuts) prior is proposed, based on the nonlocally centralized sparse representation (NCSR) model. The proposed method performs denoising and SR reconstruction simultaneously. The PGCuts prior, which is built on a novel 3D neighborhood system and a patch-group model, is able to denoise the image, restore smooth and sharp edges, etc. The edge strength measurement is introduced to adaptively balance the constraint strength of PGCuts prior in reconstruction. As PGCuts constraint is weak in smooth regions, a region-based fusion scheme is also used to further suppress the noise. Reconstruction experiments are conducted on both synthesized and real noisy LR images. It is demonstrated that the proposed method can restore plenty of details in reconstructed SR images while still suppress the noise, giving not only high scores in objective criteria like PSNR and SSIM, but also very good visual effects in non-smooth regions in subjective evaluations.关键词
超分辨率/稀疏表示/块组割/分区域融合Key words
Super-resolution (SR)/sparse representation/patch group cuts (PGCuts)/region-based fusion引用本文复制引用
李滔,何小海,卿粼波,滕奇志..基于自适应块组割先验的噪声图像超分辨率重建[J].自动化学报,2017,43(5):765-777,13.基金项目
国家自然科学基金(61471248)资助 Supported by National Natural Science Foundation of China(61471248) （61471248）
