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 451475 of 1589 papers

TitleStatusHype
EDFace-Celeb-1M: Benchmarking Face Hallucination with a Million-scale DatasetCode1
Edge-enhanced Feature Distillation Network for Efficient Super-ResolutionCode1
Scale-Equivariant Imaging: Self-Supervised Learning for Image Super-Resolution and DeblurringCode1
Semantic-Guided Diffusion Model for Single-Step Image Super-ResolutionCode1
edge-SR: Super-Resolution For The MassesCode1
EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-ResolutionCode1
EDPN: Enhanced Deep Pyramid Network for Blurry Image RestorationCode1
Real-World Light Field Image Super-Resolution via Degradation ModulationCode1
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image SynthesisCode1
Transformer for Single Image Super-ResolutionCode1
CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-ResolutionCode1
Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature LearningCode1
Learning Filter Basis for Convolutional Neural Network CompressionCode1
Learning A Single Network for Scale-Arbitrary Super-ResolutionCode1
Efficient and Degradation-Adaptive Network for Real-World Image Super-ResolutionCode1
Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-ResolutionCode1
Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale LearningCode1
Efficient CNN-based Super Resolution Algorithms for mmWave Mobile Radar ImagingCode1
Efficient Conditional Diffusion Model with Probability Flow Sampling for Image Super-resolutionCode1
Efficient Deep Models for Real-Time 4K Image Super-Resolution. NTIRE 2023 Benchmark and ReportCode1
Enhanced Deep Residual Networks for Single Image Super-ResolutionCode1
Efficient scene text image super-resolution with semantic guidanceCode1
Efficient Image Super-Resolution Using Pixel AttentionCode1
Efficient Image Super-Resolution using Vast-Receptive-Field AttentionCode1
Efficient Real-world Image Super-Resolution Via Adaptive Directional Gradient ConvolutionCode1
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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