Hybrid framework for image denoising with patch prior estimation

In this paper, a hybrid framework is proposed for image denoising, in which several state-of-the-art denoising methods are efficiently incorporated with a well trade-off by using the prior of patches. In detail, unlike modeling patches with the prior in existed denoising methods, the prior estimation here is presented only to detect the attributes of patches. Then, noisy patches are clustered into several categories according to their patch attributes. Sequentially, different denoising methods are adopted on patches of different categories. The restored image is finally synthesized with the denoised patches of all categories. Experiments show that, by using the hybrid framework, the proposed algorithm is insensitive to the variation of the attributes of images, and can robustly restore images with a remarkable denoising performance.