Motion-blurred image restoration framework based on parameter estimation and fuzzy radial basis function neural networks. (December 2022)

• Record Type: Journal Article
• Authors: Zhao, Shengmin; Oh, Sung-Kwun; Kim, Jin-Yul; Fu, Zunwei; Pedrycz, Witold
• Is Part Of: Pattern recognition. Volume 132(2022)
• Publication Date: 2022-12
• Subjects: Motion-blurred image restoration framework -- Point spread function -- Blur parameter estimation based on the particle swarm optimization -- Polynomial-based radial basis function neural network -- Image Quality Assessment; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.108983
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Overall structural framework designed for the restoration of motion-blurred images is proposed with the aid of PSO-based parameter estimation and image quality assessment. The proposed Image Restoration Framework has a complete function which can effectively enhance the restored image quality. Blur parameter estimation algorithm based on PSO (BPPO) is employed to optimize the motion-blurred parameter estimation. A polynomial-based radial basis function neural network is used as image quality evaluation method to evaluate restored image quality for efficient classification. Abstract: The restoration of motion-blurred images has always been a complex problem in image restoration. The current single blurred image algorithm cannot very well solve the estimation error of motion blur parameters. A comprehensive motion-blurred image restoration framework is proposed, which includes motion-blurred data generation, blur parameter estimation, and image quality assessment of restored images. First, we designed and used four image data sets with different degrees of blurring. We innovatively propose a blur parameter estimation algorithm based on the particle swarm optimization (B-PSO) algorithm. The Naturalness Image Quality Evaluator (NIQE) is used as the fitness function of the PSO algorithm. The framework also introduces a polynomial-based radial basis function neural network (P-RBFNN) as a new image quality assessment (IQA) method, with good image classification performance. Test results from public datasets show that the proposed framework can accurately estimate blur parameters. The peak signal-to-noise ratio (PSNR) reaches 29.976 dB, the structural similarity (SSIM) reaches 0.9044, and the classification rate is 96%. The proposed restoration framework produces the best image restoration results.