2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDIVMSP-20.4
Paper Title Image Denoising Based on Correlation Adaptive Sparse Modeling
Authors Hangfan Liu, University of Pennsylvania, United States; Jian Zhang, Chong Mou, Peking University, China
SessionIVMSP-20: Denoising and Deblurring
LocationGather.Town
Session Time:Thursday, 10 June, 13:00 - 13:45
Presentation Time:Thursday, 10 June, 13:00 - 13:45
Presentation Poster
Topic Image, Video, and Multidimensional Signal Processing: [IVTEC] Image & Video Processing Techniques
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Image restoration techniques generally use intrinsic correlations of image signals to reduce the uncertainty of the unknown signal and estimate the latent ground truth. Local and non-local correlation are the two major kinds of correlations utilized. They are different sources of correlations reflecting connections between different image data, but such a difference is not taken into consideration in most of the existing schemes. Typically, sparse representation based works use the same image data to exploit both local and non-local correlation in shared regularization. This paper aims to fully exploit local and non-local correlation of image contents separately so that near-optimal sparse representations are achieved and thus the uncertainty of signals is minimized. The proposed scheme adaptively selects different image data to exploit local and non-local correlations respectively. In particular, to exploit local correlation, the image data of interest are extracted from clustered rows of patch groups that consist of similar image contents. Experimental results on image denoising show that the proposed scheme not only outperforms state-of-the-art sparsity and low rank-based methods, but also surpasses successful deep learning-based approaches in terms of PSNR, SSIM, and visual quality.