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 951–975 of 1589 papers

TitleStatusHype
Matrix Neural Networks—0
Matrix Variate RBM and Its Applications—0
MaxSR: Image Super-Resolution Using Improved MaxViT—0
Double Sparse Multi-Frame Image Super Resolution—0
Measurement-Consistent Networks via a Deep Implicit Layer for Solving Inverse Problems—0
Medical image super-resolution method based on dense blended attention network—0
DOTE: Dual cOnvolutional filTer lEarning for Super-Resolution and Cross-Modality Synthesis in MRI—0
Training Your Image Restoration Network Better with Random Weight Network as Optimization Function—0
Memory-augmented Deep Unfolding Network for Guided Image Super-resolution—0
Perceptual Image Super-Resolution with Progressive Adversarial Network—0
DLGSANet: Lightweight Dynamic Local and Global Self-Attention Networks for Image Super-Resolution—0
Diverse super-resolution with pretrained deep hiererarchical VAEs—0
DiT4SR: Taming Diffusion Transformer for Real-World Image Super-Resolution—0
Distilling with Residual Network for Single Image Super Resolution—0
Metric Imitation by Manifold Transfer for Efficient Vision Applications—0
MFAGAN: A Compression Framework for Memory-Efficient On-Device Super-Resolution GAN—0
MFSR: Multi-fractal Feature for Super-resolution Reconstruction with Fine Details Recovery—0
Dissecting Arbitrary-scale Super-resolution Capability from Pre-trained Diffusion Generative Models—0
Micro-CT Synthesis and Inner Ear Super Resolution via Generative Adversarial Networks and Bayesian Inference—0
Mitigating Channel-wise Noise for Single Image Super Resolution—0
Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery—0
Adaptive Transform Domain Image Super-resolution Via Orthogonally Regularized Deep Networks—0
MMSR: Multiple-Model Learned Image Super-Resolution Benefiting From Class-Specific Image Priors—0
Model-Driven Channel Estimation for OFDM Systems Based on Image Super- Resolution Network—0
Modeling Deformable Gradient Compositions for Single-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