Image denoising based on learning over-complete dictionary

The sparse and redundant representations of signal theory have aroused extensive and deep research in recent years, and been widely applied to image processing. Aiming to study the performance and suitability of the sparse and redundant representations in image denoising, our paper introduces a sparse and redundant representations algorithm based on over-complete learned dictionary to process different types of images. We use the K-SVD denoising framework and modify its initial dictionary, and then mainly focus on using it to study its denoising performance and suitability for different types of images, and then compare it with some other image denoising algorithms. As to the remote sensing images denoising, the experiment results show that the K-SVD algorithm can leads to the state-of-art denoising performance at low noisy levels, but for high noisy levels, its performance isn´t good on PSNR and visual effect, that is it cannot retain the local details of images.