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

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Showing 101–150 of 1589 papers

TitleStatusHype
Visual Autoregressive Modeling for Image Super-ResolutionCode2
Rethinking the Upsampling Layer in Hyperspectral Image Super Resolution—0
HSRMamba: Contextual Spatial-Spectral State Space Model for Single Image Hyperspectral Super-ResolutionCode1
Directing Mamba to Complex Textures: An Efficient Texture-Aware State Space Model for Image Restoration—0
Efficient Attention-Sharing Information Distillation Transformer for Lightweight Single Image Super-ResolutionCode2
Binary Diffusion Probabilistic Model—0
Contrast: A Hybrid Architecture of Transformers and State Space Models for Low-Level Vision—0
NeurOp-Diff:Continuous Remote Sensing Image Super-Resolution via Neural Operator DiffusionCode1
State-of-the-Art Transformer Models for Image Super-Resolution: Techniques, Challenges, and Applications—0
Diff-Ensembler: Learning to Ensemble 2D Diffusion Models for Volume-to-Volume Medical Image Translation—0
Multi-Label Scene Classification in Remote Sensing Benefits from Image Super-Resolution—0
StructSR: Refuse Spurious Details in Real-World Image Super-ResolutionCode1
ADD: Attribution-Driven Data Augmentation Framework for Boosting Image Super-Resolution—0
VolFormer: Explore More Comprehensive Cube Interaction for Hyperspectral Image Restoration and BeyondCode1
Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual ApproximatorsCode1
TSP-Mamba: The Travelling Salesman Problem Meets Mamba for Image Super-resolution and Beyond—0
Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution—0
Flowing from Words to Pixels: A Noise-Free Framework for Cross-Modality Evolution—0
GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution—0
Zero-Shot Image Restoration Using Few-Step Guidance of Consistency Models (and Beyond)Code1
MaIR: A Locality- and Continuity-Preserving Mamba for Image RestorationCode2
YOLO-MST: Multiscale deep learning method for infrared small target detection based on super-resolution and YOLO—0
Structural Similarity in Deep Features: Image Quality Assessment Robust to Geometrically Disparate Reference—0
Uncertainty Estimation for Super-Resolution using ESRGAN—0
Flowing from Words to Pixels: A Framework for Cross-Modality Evolution—0
Plug-and-Play Tri-Branch Invertible Block for Image RescalingCode1
CLIP-SR: Collaborative Linguistic and Image Processing for Super-Resolution—0
Sequence Matters: Harnessing Video Models in 3D Super-Resolution—0
A Single-Frame and Multi-Frame Cascaded Image Super-Resolution Method—0
Super-Resolution for Remote Sensing Imagery via the Coupling of a Variational Model and Deep Learning—0
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-Resolution—0
Edge-SD-SR: Low Latency and Parameter Efficient On-device Super-Resolution with Stable Diffusion via Bidirectional Conditioning—0
LocalSR: Image Super-Resolution in Local Region—0
LossAgent: Towards Any Optimization Objectives for Image Processing with LLM AgentsCode0
Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution—0
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-resolution—0
RFSR: Improving ISR Diffusion Models via Reward Feedback LearningCode1
CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution—0
Auto-Encoded Supervision for Perceptual Image Super-ResolutionCode2
FaithDiff: Unleashing Diffusion Priors for Faithful Image Super-resolution—0
HoliSDiP: Image Super-Resolution via Holistic Semantics and Diffusion PriorCode0
Hierarchical Information Flow for Generalized Efficient Image Restoration—0
TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-ResolutionCode3
HAAT: Hybrid Attention Aggregation Transformer for Image Super-Resolution—0
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All datasetsSet14 - 4x upscalingBSD100 - 4x upscalingUrban100 - 4x upscalingManga109 - 4x upscalingSet5 - 2x upscalingSet14 - 2x upscalingSet5 - 3x upscalingBSD100 - 2x upscalingUrban100 - 2x upscalingSet14 - 3x upscalingUrban100 - 3x upscalingBSD100 - 3x upscaling

Benchmark Results

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