PSO-Optimized Blind Image Deconvolution for Improved Detectability in Poor Visual Conditions

Abstract: Image restoration is a critical step in many vision applications. Due to the poor quality of Passive Millimeter Wave (PMMW) images, especially in marine and underwater environment, developing strong algorithms for the restoration of these images is of primary importance. In addition, little information about image degradation process, which is referred to as Point Spread Function (PSF), makes the problem more challenging. Blind image deconvolution is a popular approach for image restoration, which can estimate the original image and the degradation function simultaneously. This is an ill-posed inverse problem and requires regularization to be solved. In addition to the type of regularization functions, the value of regularization parameters can drastically affect the output result. In this paper, we propose an optimized main function for improving the resolution of Passive Millimeter Wave (PMMW) images based on the semi-blind deconvolution and propose a Particle Swarm Optimization (PSO) algorithm for selecting optimum values of regularization parameters in blind image deconvolution. A new cost function is defined for the optimization process which is useful in image restoration. The algorithm has been tested on standard images and evaluated using standard metrics. Two real PMMW images blurred by an unknown degradation function are also used in this algorithm to obtain a sharp deblurred image with an estimate of the PSF. Simulation results show that the proposed method improves the quality of the estimated PSF and the deblurred image.

Keywords:

Blind Deconvolution , PSO algorithm , PSF estimation , PMMW images , regularization parameter.‎