Unknown noise removal via sparse representation model

To remove more complex or unknown noise, we propose a new dictionary learning model by assuming noise as Mixture of Gaussian (MoG) distributions. Since MoG is able to approximate any continuous distributions universally, the proposed method can effectively recover the original image from the corrupted one with various forms of noise. Meanwhile, to solve weighted ℓ₂-ℓ₀ minimization problems, we further propose modified orthogonal matching pursuit method in sparse coding and extend alternating proximal algorithm to update dictionaries. Experimental results demonstrate that our proposed method is superior to several previous denoising methods in terms of quantitative measures and visual quality.