Total variation reconstruction for compressive sensing using nonlocal Lagrangian multiplier
Year: 2016
Type: preprint
Abstract: Total variation has proved its effectiveness in solving inverse problems for compressive sensing. Besides, the nonlocal means filter used as regularization preserves texture better for recovered images, but it is quite complex to implement. In this paper, based on existence of both noise and image information in the Lagrangian multiplier, we propose a simple method in term of implementation called nonlocal Lagrangian multiplier (NLLM) in order to reduce noise and boost useful image information. Experimental results show that the proposed NLLM is superior both in subjective and objective qualities of recovered image over other recovery algorithms.
Source: arXiv (Cornell University)
Authors: Van Chien, Trinh, Dinh, Khanh Quoc, Nguyen, Viet Anh, Jeon, Byeungwoo
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2398498549