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

Showing 651–700 of 1589 papers

TitleStatusHype
Deep Laplacian Pyramid Network for Text Images Super-ResolutionCode0
Deep Iterative Residual Convolutional Network for Single Image Super-ResolutionCode0
Hierarchical Back Projection Network for Image Super-ResolutionCode0
HF-Diff: High-Frequency Perceptual Loss and Distribution Matching for One-Step Diffusion-Based Image Super-ResolutionCode0
A Lightweight Image Super-Resolution Transformer Trained on Low-Resolution Images OnlyCode0
HASN: Hybrid Attention Separable Network for Efficient Image Super-resolutionCode0
Beyond Subspace Isolation: Many-to-Many Transformer for Light Field Image Super-resolutionCode0
ML-CrAIST: Multi-scale Low-high Frequency Information-based Cross black Attention with Image Super-resolving TransformerCode0
Deep Fusion Prior for Plenoptic Super-Resolution All-in-Focus ImagingCode0
Guidance Disentanglement Network for Optics-Guided Thermal UAV Image Super-ResolutionCode0
Deep Fourier Up-SamplingCode0
Modulating Image Restoration with Continual Levels via Adaptive Feature Modification LayersCode0
Multi-Level Feature Fusion Network for Lightweight Stereo Image Super-ResolutionCode0
MemNet: A Persistent Memory Network for Image RestorationCode0
Deep Decomposition Learning for Inverse Imaging ProblemsCode0
GhostSR: Learning Ghost Features for Efficient Image Super-ResolutionCode0
Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold SimplificationCode0
MaskBlur: Spatial and Angular Data Augmentation for Light Field Image Super-ResolutionCode0
Maintaining Natural Image Statistics with the Contextual LossCode0
Deep Burst DenoisingCode0
MAANet: Multi-view Aware Attention Networks for Image Super-ResolutionCode0
Localized Super Resolution for Foreground Images using U-Net and MR-CNNCode0
LossAgent: Towards Any Optimization Objectives for Image Processing with LLM AgentsCode0
Multi-level Wavelet-CNN for Image RestorationCode0
Deep Bi-Dense Networks for Image Super-ResolutionCode0
Generative Collaborative Networks for Single Image Super-ResolutionCode0
Deep Back-Projection Networks For Super-ResolutionCode0
Deep Back-Projection Networks for Single Image Super-resolutionCode0
Generative Adversarial Networks: An OverviewCode0
Generative adversarial network-based image super-resolution using perceptual content lossesCode0
Deep Learning-Based Channel EstimationCode0
High-throughput, high-resolution registration-free generated adversarial network microscopyCode0
Lightweight Feature Fusion Network for Single Image Super-ResolutionCode0
Adaptive Densely Connected Super-Resolution ReconstructionCode0
Lightweight and Efficient Image Super-Resolution with Block State-based Recursive NetworkCode0
Lightweight Image Super-Resolution with Adaptive Weighted Learning NetworkCode0
Gated Multiple Feedback Network for Image Super-ResolutionCode0
Gated Fusion Network for Joint Image Deblurring and Super-ResolutionCode0
DDR: Exploiting Deep Degradation Response as Flexible Image DescriptorCode0
AIM 2019 Challenge on Constrained Super-Resolution: Methods and ResultsCode0
AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and ResultsCode0
Frequency Separation for Real-World Super-ResolutionCode0
Data-Free Knowledge Distillation for Image Super-ResolutionCode0
Learning Series-Parallel Lookup Tables for Efficient Image Super-ResolutionCode0
Data Upcycling Knowledge Distillation for Image Super-ResolutionCode0
Learning Parallax Attention for Stereo Image Super-ResolutionCode0
Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution ForestsCode0
Data-driven Super-Resolution of Flood Inundation Maps using Synthetic SimulationsCode0
Learning a No-Reference Quality Metric for Single-Image Super-ResolutionCode0
Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsCode0
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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
6DRCTPSNR28.06—Unverified
7CPAT+PSNR28.06—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