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 601625 of 1589 papers

TitleStatusHype
Feature Alignment with Equivariant Convolutions for Burst Image Super-Resolution0
Boosting Diffusion-Based Text Image Super-Resolution Model Towards Generalized Real-World Scenarios0
Undertrained Image Reconstruction for Realistic Degradation in Blind Image Super-Resolution0
Seeing Eye to AI? Applying Deep-Feature-Based Similarity Metrics to Information Visualization0
BadRefSR: Backdoor Attacks Against Reference-based Image Super ResolutionCode0
MFSR: Multi-fractal Feature for Super-resolution Reconstruction with Fine Details Recovery0
MambaLiteSR: Image Super-Resolution with Low-Rank Mamba using Knowledge Distillation0
Data-driven Super-Resolution of Flood Inundation Maps using Synthetic SimulationsCode0
Image Super-Resolution with Guarantees via Conformalized Generative Models0
Fast Omni-Directional Image Super-Resolution: Adapting the Implicit Image Function with Pixel and Semantic-Wise Spherical Geometric PriorsCode0
A Statistical Learning Perspective on Semi-dual Adversarial Neural Optimal Transport Solvers0
Exploring Linear Attention Alternative for Single Image Super-ResolutionCode0
Rethinking the Upsampling Layer in Hyperspectral Image Super Resolution0
Directing Mamba to Complex Textures: An Efficient Texture-Aware State Space Model for Image Restoration0
Binary Diffusion Probabilistic Model0
Contrast: A Hybrid Architecture of Transformers and State Space Models for Low-Level Vision0
State-of-the-Art Transformer Models for Image Super-Resolution: Techniques, Challenges, and Applications0
Diff-Ensembler: Learning to Ensemble 2D Diffusion Models for Volume-to-Volume Medical Image Translation0
Multi-Label Scene Classification in Remote Sensing Benefits from Image Super-Resolution0
ADD: Attribution-Driven Data Augmentation Framework for Boosting Image Super-Resolution0
Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution0
Flowing from Words to Pixels: A Noise-Free Framework for Cross-Modality Evolution0
TSP-Mamba: The Travelling Salesman Problem Meets Mamba for Image Super-resolution and Beyond0
GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution0
Structural Similarity in Deep Features: Image Quality Assessment Robust to Geometrically Disparate Reference0
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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
8CPAT+PSNR29.36Unverified
9SwinFIRPSNR29.36Unverified
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