当前位置 >>成果展示 >>论文

Graph-Based Joint Dequantization and Contrast Enhancement of Poorly Lit JPEG Images.(IEEE Transactions on Image Processing 2019)时间: 2019-03-28 点击: 169 次


Authors: Xianming Liu ; Gene Cheung ; Xiangyang Ji ; Debin Zhao ; Wen Gao 

Abstract:

JPEG images captured in poor lighting conditions suffer from both low luminance contrast and coarse quantization artifacts due to lossy compression. Performing dequantization and contrast enhancement in separate back-to-back steps would amplify the residual compression artifacts, resulting in low visual quality. Leveraging on recent development in graph signal processing (GSP), we propose to jointly dequantize and contrast-enhance such images in a single graph-signal restoration framework. Specifically, we separate each observed pixel patch into illumination and reflectance via Retinex theory, where we define generalized smoothness prior and signed graph smoothness prior according to their respective unique signal characteristics. Given only a transform-coded image patch, we compute robust edge weights for each graph via low-pass filtering in the dual graph domain. We compute the illumination and reflectance components for each patch alternately, adopting accelerated proximal gradient (APG) algorithms in the transform domain, with backtracking line search for further speedup. Experimental results show that our generated images outperform the state-of-the-art schemes noticeably in the subjective quality evaluation.


Published in: IEEE Transactions on Image Processing ( Volume: 28 , Issue: 3 , March 2019 )
Page(s): 1205 - 1219
Date of Publication: 28 September 2018
ISSN Information:
INSPEC Accession Number: 18194509
Publisher: IEEE
Funding Agency:
上一篇:Depth Super-Resolution via Joint Color-Guided Internal and External Regularizations.(IEEE Transactions on Image Processing 2019) 下一篇:Depth Restoration From RGB-D Data via Joint Adaptive Regularization and Thresholding on Manifolds.(IEEE Transactions on Image Processing 2019) 返回列表

用户登录

用户注册