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 1526–1550 of 1589 papers

TitleStatusHype
Image Denoising and Super-Resolution using Residual Learning of Deep Convolutional Network—0
The Power of Context: How Multimodality Improves Image Super-Resolution—0
Image Inpainting for High-Resolution Textures using CNN Texture Synthesis—0
ImagePairs: Realistic Super Resolution Dataset via Beam Splitter Camera Rig—0
Image Processing GNN: Breaking Rigidity in Super-Resolution—0
Wavelet-SRNet: A Wavelet-Based CNN for Multi-Scale Face Super Resolution—0
Image Reconstruction of Multi Branch Feature Multiplexing Fusion Network with Mixed Multi-layer Attention—0
A Generalized Tensor Formulation for Hyperspectral Image Super-Resolution Under General Spatial Blurring—0
Image Resolution Enhancement by Using Interpolation Followed by Iterative Back Projection—0
Progressive Image Super-Resolution via Neural Differential Equation—0
Fingerprints of Super Resolution Networks—0
Application of convolutional neural networks in image super-resolution—0
Fine-tuned Generative Adversarial Network-based Model for Medical Image Super-Resolution—0
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain—0
Time accelerated image super-resolution using shallow residual feature representative network—0
Image Super-Resolution Based on Sparsity Prior via Smoothed l_0 Norm—0
Time-lapse image classification using a diffractive neural network—0
Super-Resolution Generative Adversarial Networks based Video Enhancement—0
Fine-Grained Neural Architecture Search—0
Image super-resolution reconstruction based on attention mechanism and feature fusion—0
Image Super-resolution Reconstruction Network based on Enhanced Swin Transformer via Alternating Aggregation of Local-Global Features—0
Image Super-Resolution Using Attention Based DenseNet with Residual Deconvolution—0
WDN: A Wide and Deep Network to Divide-and-Conquer Image Super-resolution—0
Fidelity-Naturalness Evaluation of Single Image Super Resolution—0
Toward Real-world Image Super-resolution via Hardware-based Adaptive Degradation Models—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