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A Study of the Limits of Collaborative DCT-Based Image Denoising via Interpretable Neural Networks
Image denoising remains a fundamental problem in image restoration, with applications in photography, biomedical, and scientific imaging. Modern deep neural networks achieve strong performance by learning powerful image priors, but often rely on large black-box models with limited interpretability.
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