SOTAVerified

Image Super-Resolution

Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image. This task can be used for various applications such as improving image quality, enhancing visual detail, and increasing the accuracy of computer vision algorithms.

Papers

Showing 126150 of 1589 papers

TitleStatusHype
Plug-and-Play Tri-Branch Invertible Block for Image RescalingCode1
CLIP-SR: Collaborative Linguistic and Image Processing for Super-Resolution0
Sequence Matters: Harnessing Video Models in 3D Super-Resolution0
A Single-Frame and Multi-Frame Cascaded Image Super-Resolution Method0
Super-Resolution for Remote Sensing Imagery via the Coupling of a Variational Model and Deep Learning0
Arbitrary-steps Image Super-resolution via Diffusion InversionCode5
OFTSR: One-Step Flow for Image Super-Resolution with Tunable Fidelity-Realism Trade-offsCode1
RealOSR: Latent Unfolding Boosting Diffusion-based Real-world Omnidirectional Image Super-ResolutionCode0
RAP-SR: RestorAtion Prior Enhancement in Diffusion Models for Realistic Image Super-ResolutionCode1
MPSI: Mamba enhancement model for pixel-wise sequential interaction Image Super-Resolution0
Edge-SD-SR: Low Latency and Parameter Efficient On-device Super-Resolution with Stable Diffusion via Bidirectional Conditioning0
LocalSR: Image Super-Resolution in Local Region0
LossAgent: Towards Any Optimization Objectives for Image Processing with LLM AgentsCode0
Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution0
TASR: Timestep-Aware Diffusion Model for Image Super-ResolutionCode1
Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA ApproachCode4
HIIF: Hierarchical Encoding based Implicit Image Function for Continuous Super-resolution0
RFSR: Improving ISR Diffusion Models via Reward Feedback LearningCode1
CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution0
Auto-Encoded Supervision for Perceptual Image Super-ResolutionCode2
FaithDiff: Unleashing Diffusion Priors for Faithful Image Super-resolution0
HoliSDiP: Image Super-Resolution via Holistic Semantics and Diffusion PriorCode0
Hierarchical Information Flow for Generalized Efficient Image Restoration0
TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-ResolutionCode3
HAAT: Hybrid Attention Aggregation Transformer for Image Super-Resolution0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR29.54Unverified
2HMA†PSNR29.51Unverified
3Hi-IR-LPSNR29.49Unverified
4HAT-LPSNR29.47Unverified
5HAT_FIRPSNR29.44Unverified
6DRCTPSNR29.4Unverified
7HATPSNR29.38Unverified
8SwinFIRPSNR29.36Unverified
9CPAT+PSNR29.36Unverified
10CPATPSNR29.34Unverified
#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR28.16Unverified
2HMA†PSNR28.13Unverified
3Hi-IR-LPSNR28.13Unverified
4HAT-LPSNR28.09Unverified
5HAT_FIRPSNR28.07Unverified
6CPAT+PSNR28.06Unverified
7DRCTPSNR28.06Unverified
8HATPSNR28.05Unverified
9CPATPSNR28.04Unverified
10SwinFIRPSNR28.03Unverified
#ModelMetricClaimedVerifiedStatus
1Hi-IR-LPSNR28.72Unverified
2DRCT-LPSNR28.7Unverified
3HMA†PSNR28.69Unverified
4HAT-LPSNR28.6Unverified
5HAT_FIRPSNR28.43Unverified
6DRCTPSNR28.4Unverified
7HATPSNR28.37Unverified
8CPAT+PSNR28.33Unverified
9CPATPSNR28.22Unverified
10PFTPSNR28.2Unverified