Enhancing Image Quality by Minimizing Specular Reflections via Dark Channel Prior

The goal of blind picture deblurring, a challenging and demanding topic in image processing, is to restore the original, clear image from a deteriorated and blurry version without requiring prior knowledge either the blur kernel or the clean image. In this research, we describe an efficient approach to blind image deblurring that combines matrix-variable optimization with two-dimensional discrete wavelet transform (DWT) to provide better deblurring results. We can immediately optimize a matrix representation of the clean image thanks to the matrix-variable optimization framework, which allows for more accurate and efficient optimization. Additionally, by employing the DWT to divide the estimated clean picture matrix into many frequency sub bands, high-frequency noise components can be better deblurred by regularizing and denoising them.