Fast and Adaptive Boosting Techniques for Variational Based Image Restoration

IEEE Access

Sabit Rahim¹  Raheel Ahmed Memon²  Abdul Shakoor³  Chunming Li⁴  Abdul Basit⁴  Samad Wali⁴  Samina Samina⁵ 

[1] Department of Computer Science, Karakoram International University, Gilgit, Pakistan;Department of Computer Science, Sukkur IBA University, Sindh, Pakistan;Department of Mathematics, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan;School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China;School of Mathematics, Southwest Jiaotong University, Chengdu, China;

关键词: Boosting techniques;    variational models;    image restoration;    alternating direction method of multipliers;   

DOI  :  10.1109/ACCESS.2019.2959003

来源: DOAJ

【 摘 要 】

Variational based problems are an important class of problems and have a space of improvement in image processing. Boosting techniques have been shown capable of improving many image restoration algorithms. This paper considers four fast and adaptive boosting techniques for variational based image restoration. The adaptive boosting frameworks can compute the existing image restoration algorithm iteratively. The primary idea is to get an enhanced result by using the output of the current step as a part of the input for the next step. Our techniques can boost variational based regularization models like total variation (TV) and total generalized variation (TGV). For image restoration, we used an adaptive regularization parameter selection, which produces signals with more details and preserves tiny objects. For efficient numerical optimization, we implement the alternating direction method of multipliers (ADMM) and demonstrate the effectiveness of the proposed techniques with a variety of experimental results. The simulation results show that the proposed boosting techniques achieve a better restoration performance on comparisons with TV and TGV in terms of quality metrics such as signal to noise ratio (SNR) and structure similarity (SSIM).

【 授权许可】

Unknown