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Found 6 papers, 4 papers with code
Fast and Interpretable Nonlocal Neural Networks for Image Denoising via Group-Sparse Convolutional Dictionary Learning
Nonlocal self-similarity within natural images has become an increasingly popular prior in deep-learning models.
Ranked #5 on
Grayscale Image Denoising
on BSD68 sigma50
Gabor is Enough: Interpretable Deep Denoising with a Gabor Synthesis Dictionary Prior
Gabor-like filters have been observed in the early layers of CNN classifiers and even throughout low-level image processing networks.
CDLNet: Noise-Adaptive Convolutional Dictionary Learning Network for Blind Denoising and Demosaicing
In this work, we propose an unrolled convolutional dictionary learning network (CDLNet) and demonstrate its competitive denoising and joint denoising and demosaicing (JDD) performance both in low and high parameter count regimes.
CDLNet: Robust and Interpretable Denoising Through Deep Convolutional Dictionary Learning
In addition, we leverage the model's interpretable construction to propose an augmentation of the network's thresholds that enables state-of-the-art blind denoising performance and near-perfect generalization on noise-levels unseen during training.
Ranked #9 on
Grayscale Image Denoising
on BSD68 sigma25
Frequency Regularized Deep Convolutional Dictionary Learning and Application to Blind Denoising
Sparse representation via a learned dictionary is a powerful prior for natural images.
Masked-RPCA: Sparse and Low-rank Decomposition Under Overlaying Model and Application to Moving Object Detection
Robust Principal Component Analysis (RPCA) performs low-rank and sparse decomposition and accomplishes such a task when the background is stationary and the foreground is dynamic and relatively small.
