An improvement of visual perception of single image by fusion of DCP with patch processing

Abstract

Scattering of atmospheric particles in hazy or foggy weather conditions is the primary reason for the degradation of images. Though a level of saturation has been obtained in the improvement of restoration model to overcome the visual vividness those termed as a dehazing method, there still remains a void while restoring of the digital image. We, in this paper, have proposed a novel and efficient haze removal algorithm by considering an efficient prior knowledge like DCP in order to eliminate haze and, restore albedo by combined with diffusion patch process after soft mating to enhance again for better clarity and accuracy of a hazy input image. In this research analysis, we critically examined popular Dark Channel Prior haze resisting removals algorithm such as Fattal, He, Tan and Tarel with our proposed novel haze removal model, The image quality assessment such as qualitative and quantitative index of the de-hazed image is concerned with estimating the parameters such as RSME, PSNR, SSIM, FSIM, and FSIMc are also reported for measuring precise accuracy of our proposed methodology as compared to the existing one. This novel method found to be distinctive and ensures efficient production of a high-quality haze-free image.