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 651–700 of 1589 papers

TitleStatusHype
CurvPnP: Plug-and-play Blind Image Restoration with Deep Curvature DenoiserCode0
A Comprehensive Survey of Transformers for Computer Vision—0
Contrastive Learning for Climate Model Bias Correction and Super-Resolution—0
The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution NetworksCode1
RRSR:Reciprocal Reference-based Image Super-Resolution with Progressive Feature Alignment and Selection—0
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: ReportCode1
Underwater Image Super-Resolution using Generative Adversarial Network-based Model—0
Measurement-Consistent Networks via a Deep Implicit Layer for Solving Inverse Problems—0
Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery—0
HyperSound: Generating Implicit Neural Representations of Audio Signals with Hypernetworks—0
Fine-tuned Generative Adversarial Network-based Model for Medical Image Super-Resolution—0
Combining Attention Module and Pixel Shuffle for License Plate Super-ResolutionCode1
Iris super-resolution using CNNs: is photo-realism important to iris recognition?—0
Single Image Super-Resolution via a Dual Interactive Implicit Neural NetworkCode1
How Real is Real: Evaluating the Robustness of Real-World Super Resolution—0
Boomerang: Local sampling on image manifolds using diffusion models—0
Single Image Super-Resolution Using Lightweight Networks Based on Swin Transformer—0
Real Image Super-Resolution using GAN through modeling of LR and HR process—0
ITSRN++: Stronger and Better Implicit Transformer Network for Continuous Screen Content Image Super-Resolution—0
ISTA-Inspired Network for Image Super-Resolution—0
Scene Text Image Super-Resolution via Content Perceptual Loss and Criss-Cross Transformer Blocks—0
CUF: Continuous Upsampling Filters—0
Efficient Image Super-Resolution using Vast-Receptive-Field AttentionCode1
Deep Fourier Up-SamplingCode0
DA-VSR: Domain Adaptable Volumetric Super-Resolution For Medical Images—0
Single Image Super-Resolution Based on Capsule Neural NetworksCode1
MuS2: A Real-World Benchmark for Sentinel-2 Multi-Image Super-ResolutionCode0
Accurate Image Restoration with Attention Retractable TransformerCode1
From Face to Natural Image: Learning Real Degradation for Blind Image Super-ResolutionCode1
Multi-scale Attention Network for Single Image Super-ResolutionCode1
Effective Invertible Arbitrary Image Rescaling—0
A heterogeneous group CNN for image super-resolutionCode1
Real-RawVSR: Real-World Raw Video Super-Resolution with a Benchmark DatasetCode1
Face Super-Resolution Using Stochastic Differential EquationsCode1
Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and RestorationCode2
Recurrent Super-Resolution Method for Enhancing Low Quality Thermal Facial Data—0
KXNet: A Model-Driven Deep Neural Network for Blind Super-ResolutionCode1
MMSR: Multiple-Model Learned Image Super-Resolution Benefiting From Class-Specific Image Priors—0
Lightweight Spatial-Channel Adaptive Coordination of Multilevel Refinement Enhancement Network for Image Reconstruction—0
Generative Adversarial Super-Resolution at the Edge with Knowledge DistillationCode1
Diffusion Models: A Comprehensive Survey of Methods and ApplicationsCode4
XCAT -- Lightweight Quantized Single Image Super-Resolution using Heterogeneous Group Convolutions and Cross Concatenation—0
QuantNAS for super resolution: searching for efficient quantization-friendly architectures against quantization noiseCode0
Joint Learning Content and Degradation Aware Feature for Blind Super-ResolutionCode1
DSR: Towards Drone Image Super-ResolutionCode1
SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-ResolutionCode1
AT-DDPM: Restoring Faces degraded by Atmospheric Turbulence using Denoising Diffusion Probabilistic ModelsCode1
RZSR: Reference-based Zero-Shot Super-Resolution with Depth Guided Self-ExemplarsCode1
Sliding Window Recurrent Network for Efficient Video Super-ResolutionCode1
Fast Nearest Convolution for Real-Time Efficient Image Super-ResolutionCode1
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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