The content delves into Decomposition Ascribed Synergistic Learning (DASL) for unified image restoration. By analyzing various degradation types through singular value decomposition, the method optimizes degraded singular vectors and values. The proposed operators, SVEO and SVAO, enhance decomposed optimization. Extensive experiments validate the effectiveness of DASL across multiple restoration tasks.
The paper introduces a novel approach to image restoration by leveraging singular value decomposition to understand degradation types better. By optimizing degraded singular vectors and values separately, the method achieves improved results. The integration of SVEO and SVAO operators enhances the decomposed optimization process. Overall, DASL demonstrates promising results across various image restoration tasks.
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