Removal of Image Blurring and Salt Pepper Noise Using Variation Models

For the past recent decades, image denoising has been analyzed in many fields such as computer vision, statistical signal and image processing. It facilitates an appropriate base for the analysis of natural image models and signal separation algorithms. Moreover, it also turns into an essential part to digital image acquiring systems to improve qualities of image. These two directions are vital and will be examined in this work. Noise and Blurring of images are two degrading factors and when image is corrupted with both blurring and mixed noises de-noising and de-blurring of image is very difficult. In this paper, Gauss-Total Variation model (G-TV model) and Gaussian Mixture-Total Variation Model (GM-TV Model) are discussed and results are presented and it is shown that blurring of image is completely removed using G-TV model; however, image corrupted with blurring and mixed noise can be recovered with GM-TV model and using DCT runtime can be reduced significantly.