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 1501–1550 of 1589 papers

TitleStatusHype
Image Super-Resolution via Deep Recursive Residual NetworkCode0
Multi-frame image super-resolution with fast upscaling technique—0
DOTE: Dual cOnvolutional filTer lEarning for Super-Resolution and Cross-Modality Synthesis in MRI—0
LAP: a Linearize and Project Method for Solving Inverse Problems with Coupled VariablesCode0
Single Image Super-Resolution Using Multi-Scale Convolutional Neural Network—0
Single Image Super-Resolution based on Wiener Filter in Similarity Domain—0
Deep Laplacian Pyramid Networks for Fast and Accurate Super-ResolutionCode0
Detail-revealing Deep Video Super-resolutionCode0
Single Image Super Resolution - When Model Adaptation Matters—0
Effect of Super Resolution on High Dimensional Features for Unsupervised Face Recognition in the Wild—0
Single Image Super-resolution via a Lightweight Residual Convolutional Neural Network—0
Single image super-resolution using self-optimizing mask via fractional-order gradient interpolation and reconstruction—0
Convolutional Low-Resolution Fine-Grained Classification—0
GUN: Gradual Upsampling Network for Single Image Super-Resolution—0
Local Patch Encoding-Based Method for Single Image Super-Resolution—0
DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution—0
Manifold Based Low-rank Regularization for Image Restoration and Semi-supervised Learning—0
Language Independent Single Document Image Super-Resolution using CNN for improved recognition—0
Super-resolution Using Constrained Deep Texture Synthesis—0
Dual Recovery Network with Online Compensation for Image Super-Resolution—0
Joint Dictionary Learning for Example-based Image Super-resolution—0
Learning a Mixture of Deep Networks for Single Image Super-Resolution—0
EnhanceNet: Single Image Super-Resolution Through Automated Texture SynthesisCode0
Learning a No-Reference Quality Metric for Single-Image Super-ResolutionCode0
Learning Parametric Sparse Models for Image Super-Resolution—0
Texture Enhancement via High-Resolution Style Transfer for Single-Image Super-Resolution—0
Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold SimplificationCode0
Amortised MAP Inference for Image Super-resolution—0
Sparsity-based Color Image Super Resolution via Exploiting Cross Channel Constraints—0
Transport-based analysis, modeling, and learning from signal and data distributions—0
A Boosting Method to Face Image Super-resolution—0
ATGV-Net: Accurate Depth Super-Resolution—0
Generic 3D Convolutional Fusion for image restoration—0
End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks—0
Local- and Holistic- Structure Preserving Image Super Resolution via Deep Joint Component Learning—0
Deep Depth Super-Resolution : Learning Depth Super-Resolution using Deep Convolutional Neural Network—0
Robust Single Image Super-Resolution via Deep Networks With Sparse Prior—0
RAISR: Rapid and Accurate Image Super Resolution—0
End-to-End Kernel Learning with Supervised Convolutional Kernel Networks—0
Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution—0
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip ConnectionsCode0
Image Super-Resolution Based on Sparsity Prior via Smoothed l_0 Norm—0
Con-Patch: When a Patch Meets its Context—0
Patch-Ordering as a Regularization for Inverse Problems in Image ProcessingCode0
Super-resolution reconstruction of hyperspectral images via low rank tensor modeling and total variation regularization—0
Matrix Neural Networks—0
Matrix Variate RBM and Its Applications—0
Image Resolution Enhancement by Using Interpolation Followed by Iterative Back Projection—0
Double Sparse Multi-Frame Image Super Resolution—0
Learning Parametric Distributions for Image Super-Resolution: Where Patch Matching Meets Sparse Coding—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
8SwinFIRPSNR29.36—Unverified
9CPAT+PSNR29.36—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