| Abstract: |
| In variational-based image denoising, the regularizer derived from mixture distributions plays a crucial role in preserving image details. However, this type of mixture distribution priors has not been incorporated into deep learning-based denoising methods. In this talk, we give a method for learning regularizers based on a learnable Laplacian mixture distribution for image denoising. Our approach is motivated by the assumption that deep image features follow a latent distribution with a mixture model. To address this assumption, we propose a regularizer with learnable weights by considering the dual problem of maximum likelihood estimation for the deep features. The dual variable in this problem represents an attention weight, which can be learned using a numerical scheme with an unrolling technique. Notably, our method establishes a connection between the mixture distribution prior and the popular attention mechanism in deep learning. The results demonstrate the superior performance of our approach for image denoising. |
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