
Deep neural networks, such as U-Net, can effectively denoise images by learning to estimate and remove the noise component, outperforming classical denoising methods based on Fourier analysis and wavelet transforms.


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denoising-images-with-classical-methods-and-deep-convolutional-neural-networks


Denoising Images with Classical Methods and Deep Convolutional Neural Networks


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This paper proposes an improved total variation (TV) model for image denoising that combines L1 and L2 norms in the data fidelity term, demonstrating superior performance compared to classic TV models and effectiveness in removing various noise types.


an-improved-total-variation-model-for-image-denoising-using-mixed-l1-and-l2-norms


An Improved Total Variation Model for Image Denoising Using Mixed L1 and L2 Norms
