Compressed Image Super-resolution
6 papers with code • 24 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Compressed Image Super-resolution
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Most implemented papers
Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration
mv-lab/swin2sr
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Using this method we can tackle the major issues in training transformer vision models, such as training instability, resolution gaps between pre-training and fine-tuning, and hunger on data.
HST: Hierarchical Swin Transformer for Compressed Image Super-resolution
ustc-imcl/hst-for-compressed-image-sr
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Compressed Image Super-resolution has achieved great attention in recent years, where images are degraded with compression artifacts and low-resolution artifacts.
Super-resolving Compressed Images via Parallel and Series Integration of Artifact Reduction and Resolution Enhancement
Based on a mathematical inference model for estimating a clean low-resolution (LR) image and a clean high-resolution (HR) image from a down-sampled and compressed observation, we have designed a CISR architecture consisting of two deep neural network modules: the artefacts removal module (ARM) and the resolution enhancement module (REM).
CISRNet: Compressed Image Super-Resolution Network
agusgun/cisr
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To tackle this problem, we proposed CISRNet; a network that employs a two-stage coarse-to-fine learning framework that is mainly optimized for Compressed Image Super-Resolution Problem.
MambaCSR: Dual-Interleaved Scanning for Compressed Image Super-Resolution With SSMs
We present MambaCSR, a simple but effective framework based on Mamba for the challenging compressed image super-resolution (CSR) task.
Plug-and-Play Tri-Branch Invertible Block for Image Rescaling
High-resolution (HR) images are commonly downscaled to low-resolution (LR) to reduce bandwidth, followed by upscaling to restore their original details.
