arXiv Computer Vision
Enhancing Low-resolution Image Representation Through Normalizing Flows
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arXiv Computer Vision
Sep 1
Efficient JPEG Restoration in the Wavelet Domain via Mean Flows
arXiv:2608.28730v1 Announce Type: cross Abstract: Latest JPEG restoration systems achieve strong quality with large models, yet often remain too slow and expensive for efficient on-device deployment....
arXiv Computer Vision
Sep 18
Training-Adaptive Convolutional Sparse Coding via Information Bottleneck for Robust Visual Representation
The paper introduces a training‑adaptive convolutional sparse coding (CSC) framework that learns the sparsity coefficient jointly with network parameters using an unfolded FISTA optimization. By treating the coefficient as a differentiable variable, the method balances information retention and compression through an information bottleneck perspective, promoting compact yet task‑relevant representations. A label‑free post‑training strategy further adjusts compression for corrupted inputs, yielding competitive accuracy on clean data and enhanced robustness to perturbations on CIFAR and ImageNet.
arXiv AI
Aug 3
WaiT for the Signal: Simple Frequency-Aware Flow-Matching
arXiv:2607. 28760v1 Announce Type: cross Abstract: As image generation models scale to ever higher resolutions, global coherence, local detail, and texture fidelity become critical axes for generation quality.
arXiv AI
Jul 29
ScaleResfusion: Residual Rectified Flow based on Residual Vector Field
arXiv:2607. 25275v1 Announce Type: cross Abstract: Real-world Image Restoration (Real-IR) aims to recover high-quality (HQ) images from complex and unknown degradations.
arXiv Computer Vision
Aug 28
High-Frequency First: A Two-Stage Approach for Improving Image INR
The paper proposes a two-stage training strategy for Implicit Neural Representations (INRs) that addresses spectral bias by using a neighbor-aware soft mask to emphasize high-frequency details early in training. In the first stage, the mask assigns higher weights to pixels with strong local variations, encouraging the network to focus on fine edges and textures. The second stage transitions to full-image training, and experiments show consistent improvements in reconstruction quality across existing INR methods.
arXiv Computer Vision
Sep 17
Adaptive Convolutional Sparse Coding via Information Bottleneck for Robust Visual Signal Representation
arXiv:2609.19122v1 Announce Type: new Abstract: Visual signals require compact yet sufficient representations for robust downstream prediction. Convolutional sparse coding (CSC) provides an explicit...