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 1351–1400 of 1589 papers

TitleStatusHype
Super-Resolution of Brain MRI Images using Overcomplete Dictionaries and Nonlocal Similarity—0
Progressive Generative Adversarial Networks for Medical Image Super resolution—0
Deep Learning for Inverse Problems: Bounds and Regularizers—0
Medical Image Super-Resolution Using a Generative Adversarial Network—0
Progressive Image Deraining Networks: A Better and Simpler BaselineCode0
Single MR Image Super-Resolution via Channel Splitting and Serial Fusion Network—0
Learning a Deep Convolution Network with Turing Test Adversaries for Microscopy Image Super Resolution—0
How Can We Make GAN Perform Better in Single Medical Image Super-Resolution? A Lesion Focused Multi-Scale ApproachCode1
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational InferenceCode0
Image Super-Resolution as a Defense Against Adversarial AttacksCode0
Image Super-Resolution via RL-CSC: When Residual Learning Meets Convolutional Sparse CodingCode0
Residual Dense Network for Image RestorationCode0
Multimodal Sensor Fusion In Single Thermal image Super-ResolutionCode0
SREdgeNet: Edge Enhanced Single Image Super Resolution using Dense Edge Detection Network and Feature Merge Network—0
Efficient Super Resolution Using Binarized Neural Network—0
Advanced Super-Resolution using Lossless Pooling Convolutional Networks—0
Wider Channel Attention Network for Remote Sensing Image Super-resolution—0
Efficient Super Resolution For Large-Scale Images Using Attentional GAN—0
Unsupervised Degradation Learning for Single Image Super-Resolution—0
Supervised Deep Kriging for Single-Image Super-Resolution—0
Binary Document Image Super Resolution for Improved Readability and OCR PerformanceCode0
Why Are Deep Representations Good Perceptual Quality Features?—0
Lightweight and Efficient Image Super-Resolution with Block State-based Recursive NetworkCode0
Super-Resolution via Image-Adapted Denoising CNNs: Incorporating External and Internal LearningCode0
MAMNet: Multi-path Adaptive Modulation Network for Image Super-ResolutionCode0
Image Reconstruction with Predictive Filter FlowCode0
Deep Laplacian Pyramid Network for Text Images Super-ResolutionCode0
Learning Temporal Coherence via Self-Supervision for GAN-based Video GenerationCode1
Blockwise Parallel Decoding for Deep Autoregressive ModelsCode1
Bi-GANs-ST for Perceptual Image Super-resolution—0
Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution NetworkCode0
Neural Nearest Neighbors NetworksCode0
Channel Attention and Multi-level Features Fusion for Single Image Super-Resolution—0
Channel Splitting Network for Single MR Image Super-Resolution—0
Deep Learning-Based Channel EstimationCode0
Deep Bi-Dense Networks for Image Super-ResolutionCode0
Image Super-Resolution Using VDSR-ResNeXt and SRCGAN—0
Triple Attention Mixed Link Network for Single Image Super Resolution—0
Towards WARSHIP: Combining Components of Brain-Inspired Computing of RSH for Image Super Resolution—0
Theory of Generative Deep Learning : Probe Landscape of Empirical Error via Norm Based Capacity Control—0
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report—0
An Effective Single-Image Super-Resolution Model Using Squeeze-and-Excitation NetworksCode0
SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation—0
Super-Resolution via Conditional Implicit Maximum Likelihood Estimation—0
Channel-wise and Spatial Feature Modulation Network for Single Image Super-Resolution—0
A Simple Framework to Leverage State-Of-The-Art Single-Image Super-Resolution Methods to Restore Light Fields—0
Kernel based low-rank sparse model for single image super-resolution—0
Image Denoising and Super-Resolution using Residual Learning of Deep Convolutional Network—0
The 2018 PIRM Challenge on Perceptual Image Super-resolutionCode1
Dual Reconstruction Nets for Image Super-Resolution with Gradient Sensitive Loss—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