An Effective Weighted Hybrid Regularizing Approach for Image Noise Reduction

• Published in: Circuits, systems, and signal processing Vol. 38; no. 1; pp. 218 - 241
• Main Authors: Islam, Md. Robiul, Xu, Chen, Raza, Rana Aamir, Han, Yu
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2019; Springer Nature B.V
• Subjects: Circuits and Systems; Digital imaging; Electrical Engineering; Electronics and Microelectronics; Engineering; Image transmission; Instrumentation; Mathematical models; Noise; Noise reduction; Preservation; Robustness (mathematics); Signal,Image and Speech Processing; Total variation; Euler Lagrange equation; Dual projection; Image denoising; Nonconvex regularizer
• ISSN: 0278-081X; 1531-5878
• DOI: 10.1007/s00034-018-0853-1

Digital images are mostly noised due to transmission and capturing disturbances. Hence, denoising becomes a notable issue because of the necessity of removing noise before its use in any application. In denoising, the important challenge is to remove the noise while protecting true information and avoiding undesirable modification in the images. The performance of classical denoising methods including convex total variation or some nonconvex regularizers is not effective enough. Thus, it is still an ongoing research toward better denoising result. Since edge preservation is a tricky issue during denoising process, designing an appropriate regularizer for a given fidelity is a mostly crucial matter in real-world problems. Therefore, we attempt to design a robust smoothing term in energy functional so that it can reduce the possibility of discontinuity and distortion of image edge details. In this work, we introduce a new denoising technique that inherits the benefits of both convex and nonconvex regularizers. The proposed method encompasses with a novel weighted hybrid regularizer in variational framework to ensure a better trade-off between the noise removal and image edge preservation. A new algorithm based on Chambolle’s method and iteratively reweighting method is proposed to solve the model efficiently. The numerical results ensure that the proposed hybrid denoising approach can perform better than the classical convex , nonconvex regularizer -based denoising and some other methods .