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Found 7 papers, 0 papers with code
Non-Local Robust Quaternion Matrix Completion for Color Images and Videos Inpainting
A new patch group based NSS prior scheme is proposed to learn explicit NSS models of natural color images.
Unsupervised Image Deraining: Optimization Model Driven Deep CNN
Consequently, we design an optimization model-driven deep CNN in which the unsupervised loss function of the optimization model is enforced on the proposed network for better generalization.
Uncertainty-Aware Unsupervised Image Deblurring with Deep Residual Prior
To address this challenge, we suggest a dataset-free deep residual prior for the kernel induced error (termed as residual) expressed by a customized untrained deep neural network, which allows us to flexibly adapt to different blurs and images in real scenarios.
Unsupervised Deraining: Where Asymmetric Contrastive Learning Meets Self-similarity
In addition, considering that the existing real rain datasets are of low quality, either small scale or downloaded from the internet, we collect a real large-scale dataset under various rainy kinds of weather that contains high-resolution rainy images.
Low-Rank Tensor Function Representation for Multi-Dimensional Data Recovery
To break this barrier, we propose a low-rank tensor function representation (LRTFR), which can continuously represent data beyond meshgrid with infinite resolution.
H2TF for Hyperspectral Image Denoising: Where Hierarchical Nonlinear Transform Meets Hierarchical Matrix Factorization
In the t-SVD, there are two key building blocks: (i) the low-rank enhanced transform and (ii) the accompanying low-rank characterization of transformed frontal slices.
Revisiting Nonlocal Self-Similarity from Continuous Representation
Extensive multi-dimensional data processing experiments on-meshgrid (e. g., image inpainting and image denoising) and off-meshgrid (e. g., climate data prediction and point cloud recovery) validate the versatility, effectiveness, and efficiency of our CRNL as compared with state-of-the-art methods.
