arXiv Computer Vision
GraLoD: Graphics-Inspired Continuous Level-of-Detail Learning for Image Restoration
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arXiv Computer Vision
Sep 28
TaskIR: Task-Driven Image Restoration via Degradation Adaptation and Task Feedback
arXiv:2609.31170v1 Announce Type: new Abstract: Task-driven image restoration aims to improve both image quality and downstream task performance. However, existing methods predominantly focus on sing...
arXiv Computer Vision
Sep 1
Fast and Accurate Image Restoration and Generation with Rank Enhanced Linear Attention
arXiv:2505.16157v3 Announce Type: replace Abstract: Transformer-based models have made remarkable progress in image restoration (IR) tasks. However, the quadratic complexity of self-attention in Tran...
arXiv AI
Jul 10
LSRM: High-Fidelity Object-Centric Reconstruction via Scaled Context Windows
arXiv:2604. 05182v2 Announce Type: replace-cross Abstract: We introduce the Large Sparse Reconstruction Model to study how scaling transformer context windows affects feed-forward 3D reconstruction.
arXiv AI
Jun 30
Learning to Adaptively Allocate Gaussians for Arbitrary-Scale Image Super-Resolution
arXiv:2606. 29400v1 Announce Type: cross Abstract: In computer graphics, visual content is continuously warped, zoomed and resampled.
arXiv Computer Vision
Oct 21
Efficient One-Step Diffusion Restoration Model with Compact Token Compression and Linear Attention
The paper introduces SANA‑SR, an efficient one‑step diffusion restoration framework for real‑world image super‑resolution. It uses a deep compression autoencoder to reduce latent tokens by a 32× ratio, preserving essential structures and textures, and applies a linear‑attention DiT with LoRA fine‑tuning for linear‑complexity token mixing. Experiments show competitive or superior performance on benchmark datasets, and the pruned model runs in 0.019 s with 407.95 G MACs and 344 M parameters, indicating strong potential for mobile deployment.
arXiv Computer Vision
Sep 15
Restore What Matters: Lessons from Joint Restoration and Recognition
arXiv:2609.13791v1 Announce Type: new Abstract: Recognition pipelines typically adopt a restore-then-recognize workflow, yet decades of experience show that generating visually pleasing images seldom...