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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 701–725 of 1589 papers

TitleStatusHype
Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey—0
Blind Image Super-Resolution with Spatial Context Hallucination—0
Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search—0
Deep Neural Network for Fast and Accurate Single Image Super-Resolution via Channel-Attention-based Fusion of Orientation-aware Features—0
Joint Low-level and High-level Textual Representation Learning with Multiple Masking Strategies—0
Blind Image Super-resolution with Rich Texture-Aware Codebooks—0
ICF-SRSR: Invertible scale-Conditional Function for Self-Supervised Real-world Single Image Super-Resolution—0
HypervolGAN: An efficient approach for GAN with multi-objective training function—0
Joint Sub-bands Learning with Clique Structures for Wavelet Domain Super-Resolution—0
Kalman-Inspired Feature Propagation for Video Face Super-Resolution—0
Deep multi-frame face super-resolution—0
Deep MR Image Super-Resolution Using Structural Priors—0
Blind Image Super-Resolution via Contrastive Representation Learning—0
Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors—0
Blind Image Super-Resolution: A Survey and Beyond—0
Hyperspectral Image Super-Resolution via Non-Local Sparse Tensor Factorization—0
Hyperspectral Image Super-Resolution via Dual-domain Network Based on Hybrid Convolution—0
Analysis Operator Learning and Its Application to Image Reconstruction—0
Joint Face Super-Resolution and Deblurring Using a Generative Adversarial Network—0
An Advanced Features Extraction Module for Remote Sensing Image Super-Resolution—0
Deep Networks for Image Super-Resolution with Sparse Prior—0
Deep Networks for Image and Video Super-Resolution—0
Hyperspectral Image Super-resolution via Deep Spatio-spectral Convolutional Neural Networks—0
Image Denoising and Super-Resolution using Residual Learning of Deep Convolutional Network—0
Hyperspectral Image Super-Resolution in Arbitrary Input-Output Band Settings—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