Paper Info
Title
Spatially Variant Linear Representation Models For Joint Filtering
Abstract
Joint filtering mainly uses an additional guidance image as a prior and transfers its structures to the target image in the filtering process. Different from existing algorithms that rely on locally linear models or hand-designed objective functions to extract the structural information from the guidance image, we propose a new joint filter based on a spatially variant linear representation model (SVLRM), where the target image is linearly represented by the guidance image. However, the SVLRM leads to a highly illposed problem. To estimate the linear representation coefficients, we develop an effective algorithm based on a deep convolutional neural network (CNN). The proposed deep CNN (constrained by the SVLRM) is able to estimate the spatially variant linear representation coefficients which are able to model the structural information of both the guidance and input images. We show that the proposed algorithm can be effectively applied to a variety of applications, including depth/RGB image upsampling and restoration, flash/no-flash image deblurring, natural image denoising, scale-aware filtering, etc. Extensive experimental results demonstrate that the proposed algorithm performs favorably against state-of-the-art methods that have been specially designed for each task.
Year
DOI
Venue
2019
10.1109/CVPR.2019.00180
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019)
Field
DocType
ISSN
Computer vision,Pattern recognition,Computer science,Linear representation,Filter (signal processing),Artificial intelligence
Conference
1063-6919
Citations 
PageRank 
References 
2
0.37
0
Authors
6
Name
Order
Citations
PageRank
Jin-shan Pan156730.84
Jiangxin Dong2272.34
Jimmy S. J. Ren332423.85
Liang Lin43007151.07
Jinhui Tang55180212.18
Yang Ming-Hsuan615303620.69