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 1101–1150 of 1589 papers

TitleStatusHype
Single Image Super-Resolution via a Holistic Attention NetworkCode1
Transfer Learning for Protein Structure Classification at Low ResolutionCode0
Hierarchical Amortized Training for Memory-efficient High Resolution 3D GANCode1
Component Divide-and-Conquer for Real-World Image Super-ResolutionCode1
Sub-Pixel Back-Projection Network For Lightweight Single Image Super-ResolutionCode1
VarSR: Variational Super-Resolution Network for Very Low Resolution Images—0
Towards Content-Independent Multi-Reference Super-Resolution: Adaptive Pattern Matching and Feature Aggregation—0
Spatial-Angular Interaction for Light Field Image Super-ResolutionCode1
Zero-Shot Image Super-Resolution with Depth Guided Internal Degradation Learning—0
Feature Representation Matters: End-to-End Learning for Reference-based Image Super-resolution—0
Binarized Neural Network for Single Image Super Resolution—0
LatticeNet: Towards Lightweight Image Super-resolution with Lattice BlockCode0
Joint Generative Learning and Super-Resolution For Real-World Camera-Screen Degradation—0
Exploring Multi-Scale Feature Propagation and Communication for Image Super Resolution—0
Increasing the Sentinel-2 potential for marine plastic litter monitoring through image fusion techniquesCode0
Multi-Step Reinforcement Learning for Single Image Super-ResolutionCode1
Multi-Image Super-Resolution for Remote Sensing using Deep Recurrent NetworksCode1
Frequency Domain-based Perceptual Loss for Super ResolutionCode1
Sequential Hierarchical Learning with Distribution Transformation for Image Super-Resolution—0
Learning with Privileged Information for Efficient Image Super-Resolution—0
Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-ResolutionCode1
Lightweight image super-resolution with enhanced CNNCode1
Light Field Image Super-Resolution Using Deformable ConvolutionCode1
Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural NetworksCode1
Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution—0
Feedback Neural Network based Super-resolution of DEM for generating high fidelity features—0
A deep primal-dual proximal network for image restoration—0
Cross-Scale Internal Graph Neural Network for Image Super-ResolutionCode1
HypervolGAN: An efficient approach for GAN with multi-objective training function—0
SRFlow: Learning the Super-Resolution Space with Normalizing FlowCode1
Efficient Integer-Arithmetic-Only Convolutional Neural NetworksCode0
Mapping Low-Resolution Images To Multiple High-Resolution Images Using Non-Adversarial Mapping—0
iSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection NetworksCode1
Real-World Super-Resolution via Kernel Estimation and Noise InjectionCode2
Progressively Unfreezing Perceptual GAN—0
Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual LearningCode1
Exploring Sparsity in Image Super-Resolution for Efficient InferenceCode1
iSeeBetter: Spatio-temporal video super-resolution using recurrent generative back-projection networksCode1
Neural Sparse Representation for Image RestorationCode1
Learning Texture Transformer Network for Image Super-ResolutionCode1
Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars MiningCode1
Robust Reference-Based Super-Resolution With Similarity-Aware Deformable ConvolutionCode1
Residual Feature Aggregation Network for Image Super-Resolution—0
SAINT: Spatially Aware Interpolation NeTwork for Medical Slice Synthesis—0
Dual Super-Resolution Learning for Semantic SegmentationCode1
Inter-Task Association Critic for Cross-Resolution Person Re-Identification—0
Hyperspectral Image Super-resolution via Deep Spatio-spectral Convolutional Neural Networks—0
Perceptual Extreme Super Resolution Network with Receptive Field BlockCode1
Single Image Super-Resolution via Residual Neuron Attention Networks—0
Iterative Network for Image Super-ResolutionCode1
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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