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
DISCO: Distributed Inference with Sparse Communications—0
Directing Mamba to Complex Textures: An Efficient Texture-Aware State Space Model for Image Restoration—0
Burst Image Super-Resolution with Base Frame Selection—0
DIPNet: Efficiency Distillation and Iterative Pruning for Image Super-Resolution—0
Burst Image Super-Resolution via Multi-Cross Attention Encoding and Multi-Scan State-Space Decoding—0
A Novel Fast 3D Single Image Super-Resolution Algorithm—0
A Comparative Study of Feature Expansion Unit for 3D Point Cloud Upsampling—0
BUFF: Bayesian Uncertainty Guided Diffusion Probabilistic Model for Single Image Super-Resolution—0
BSRAW: Improving Blind RAW Image Super-Resolution—0
A No-Reference Deep Learning Quality Assessment Method for Super-resolution Images Based on Frequency Maps—0
DIffSteISR: Harnessing Diffusion Prior for Superior Real-world Stereo Image Super-Resolution—0
DIFFNAT: Improving Diffusion Image Quality Using Natural Image Statistics—0
Bridging the Domain Gap: A Simple Domain Matching Method for Reference-based Image Super-Resolution in Remote Sensing—0
An investigation of pre-upsampling generative modelling and Generative Adversarial Networks in audio super resolution—0
AdderSR: Towards Energy Efficient Image Super-Resolution—0
3D Photon Counting CT Image Super-Resolution Using Conditional Diffusion Model—0
Differentiable Search for Finding Optimal Quantization Strategy—0
Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks—0
Differentiable Channel Sparsity Search via Weight Sharing within Filters—0
Diff-Ensembler: Learning to Ensemble 2D Diffusion Models for Volume-to-Volume Medical Image Translation—0
Suppressing Model Overfitting for Image Super-Resolution Networks—0
DifAugGAN: A Practical Diffusion-style Data Augmentation for GAN-based Single Image Super-resolution—0
Deterministic Medical Image Translation via High-fidelity Brownian Bridges—0
Boosting Optical Character Recognition: A Super-Resolution Approach—0
ADD: Attribution-Driven Data Augmentation Framework for Boosting Image Super-Resolution—0
Detail-Enhancing Framework for Reference-Based Image Super-Resolution—0
Depth Super Resolution by Rigid Body Self-Similarity in 3D—0
Boosting Image Super-Resolution Via Fusion of Complementary Information Captured by Multi-Modal Sensors—0
Is Image Super-resolution Helpful for Other Vision Tasks?—0
ISTA-Inspired Network for Image Super-Resolution—0
Latent Multi-Relation Reasoning for GAN-Prior based Image Super-Resolution—0
Dense U-net for super-resolution with shuffle pooling layer—0
Adaptive Transform Domain Image Super-resolution Via Orthogonally Regularized Deep Networks—0
Densely Connected High Order Residual Network for Single Frame Image Super Resolution—0
Dense Dual-Attention Network for Light Field Image Super-Resolution—0
Boosting Diffusion-Based Text Image Super-Resolution Model Towards Generalized Real-World Scenarios—0
A comparative analysis of SRGAN models—0
Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild—0
Boomerang: Local sampling on image manifolds using diffusion models—0
Deform-Mamba Network for MRI Super-Resolution—0
BOLD: Boolean Logic Deep Learning—0
Anchored Regression Networks Applied to Age Estimation and Super Resolution—0
Strict Enforcement of Conservation Laws and Invertibility in CNN-Based Super Resolution for Scientific Datasets—0
Deep unfolding Network for Hyperspectral Image Super-Resolution with Automatic Exposure Correction—0
Deep Super-Resolution Network for Single Image Super-Resolution with Realistic Degradations—0
Interpreting Super-Resolution Networks with Local Attribution Maps—0
DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution—0
Deep Sampling Networks—0
Deep Residual Networks with a Fully Connected Recon-struction Layer for Single Image Super-Resolution—0
Deep Residual Axial Networks—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