Image Denoising Based on Online Dictionary Learning

Abstract

Many state-of-the-art denoising algorithms often employ dictioanary learning methods to acquire the mapping relationship between the polluted image by noise and the original clean image. It is critical to generate the appropriate dictionary in image denoising based on dictionary learning. In order to promote the denoising efficiency, the proposed algorithm employs Online Dictionary Learning (ODL) to train the overcomplete dictionaries to make the dictionary more accurate. The dictionary updating procedure is improved with a warm start. The dictionary is updated by the last computed dictionary and the current input image patches. Hence the dictionary is more accurate to get better denoising images. In the experiments, the PSNR of ODL dictionary is 0.12dB higher than SCDL and 0.21dB higher than K-SVD in average. The proposed method can eliminate the artifacts and recover the texture details efficiently.