A new image denoising algorithm based on massive image database

Image denoising is the basic problem in image processing and computer vision. Pictures are always contaminated with Gaussian noise in the capture and transmission process. The adaptive dictionary learning algorithms can remove Gaussian noise very well, but it takes a lot of time in training dictionary so that these methods cannot be applied in the actual scene. So we would like to solve the problem using massive image database. Firstly in the offline stage, hash coefficients are calculated and a dictionary is trained for every image in the database. Second, in the online stage, several reference images are searched by comparing the hash coefficients. For each given noisy block the best dictionary is selected according to the best sparse operator. Finally, all blocks are recovered using these selected dictionaries thus the denoised image is obtained by weighted averaging. Experiments show that the proposed method can remove Gaussian noise very well and preserve the details and the computation complexity is significantly reduced compared with other dictionary learning algorithms.