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 701–750 of 1589 papers

TitleStatusHype
Self-Prior Guided Mamba-UNet Networks for Medical Image Super-Resolution—0
NSD-DIL: Null-Shot Deblurring Using Deep Identity Learning—0
ASteISR: Adapting Single Image Super-resolution Pre-trained Model for Efficient Stereo Image Super-resolutionCode0
ASSR-NeRF: Arbitrary-Scale Super-Resolution on Voxel Grid for High-Quality Radiance Fields Reconstruction—0
Burst Image Super-Resolution with Base Frame Selection—0
Improving Generative Adversarial Networks for Video Super-Resolution—0
Suppressing Uncertainties in Degradation Estimation for Blind Super-Resolution—0
Learning Accurate and Enriched Features for Stereo Image Super-ResolutionCode0
Enhance the Image: Super Resolution using Artificial Intelligence in MRI—0
IG-CFAT: An Improved GAN-Based Framework for Effectively Exploiting Transformers in Real-World Image Super-ResolutionCode0
LFMamba: Light Field Image Super-Resolution with State Space Model—0
Multi-Scale Feature Fusion using Channel Transformers for Guided Thermal Image Super Resolution—0
A Dictionary Based Approach for Removing Out-of-Focus BlurCode0
Geometric Distortion Guided Transformer for Omnidirectional Image Super-Resolution—0
SatDiffMoE: A Mixture of Estimation Method for Satellite Image Super-resolution with Latent Diffusion Models—0
DDR: Exploiting Deep Degradation Response as Flexible Image DescriptorCode0
Towards Realistic Data Generation for Real-World Super-Resolution—0
Arctic Sea Ice Image Super-Resolution Based on Multi-Scale Convolution and Dual-Gating Mechanism—0
Single image super-resolution based on trainable feature matching attention networkCode0
Looks Too Good To Be True: An Information-Theoretic Analysis of Hallucinations in Generative Restoration Models—0
BOLD: Boolean Logic Deep Learning—0
Universal Robustness via Median Randomized Smoothing for Real-World Super-Resolution—0
Perceptual Fairness in Image Restoration—0
Infrared Image Super-Resolution via Lightweight Information Split Network—0
Frequency-Domain Refinement with Multiscale Diffusion for Super Resolution—0
Perception- and Fidelity-aware Reduced-Reference Super-Resolution Image Quality Assessment—0
Large coordinate kernel attention network for lightweight image super-resolution—0
NAFRSSR: a Lightweight Recursive Network for Efficient Stereo Image Super-ResolutionCode0
Multi-Level Feature Fusion Network for Lightweight Stereo Image Super-ResolutionCode0
An Advanced Features Extraction Module for Remote Sensing Image Super-Resolution—0
Single Image Super-Resolution Based on Global-Local Information Synergy—0
Detail-Enhancing Framework for Reference-Based Image Super-Resolution—0
Federated Learning for Blind Image Super-Resolution—0
Deep learning-based blind image super-resolution with iterative kernel reconstruction and noise estimationCode0
Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey—0
SEGSRNet for Stereo-Endoscopic Image Super-Resolution and Surgical Instrument Segmentation—0
Single-sample image-fusion upsampling of fluorescence lifetime images—0
OmniSSR: Zero-shot Omnidirectional Image Super-Resolution using Stable Diffusion Model—0
MTKD: Multi-Teacher Knowledge Distillation for Image Super-Resolution—0
Differentiable Search for Finding Optimal Quantization Strategy—0
Fortifying Fully Convolutional Generative Adversarial Networks for Image Super-Resolution Using Divergence Measures—0
Efficient Learnable Collaborative Attention for Single Image Super-Resolution—0
Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution—0
RefQSR: Reference-based Quantization for Image Super-Resolution Networks—0
Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network—0
A Study in Dataset Pruning for Image Super-Resolution—0
Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolution—0
PAON: A New Neuron Model using Padé Approximants—0
CasSR: Activating Image Power for Real-World Image Super-Resolution—0
Arbitrary-Scale Image Generation and Upsampling using Latent Diffusion Model and Implicit Neural Decoder—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