Motion-blurred image restoration framework based on parameter estimation and fuzzy radial basis function neural networks

作者：

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.

摘要

•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.

论文关键词：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

论文评审过程：Received 26 April 2022, Revised 28 July 2022, Accepted 13 August 2022, Available online 14 August 2022, Version of Record 19 August 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108983