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 701–750 of 1589 papers

TitleStatusHype
Hybrid Function Sparse Representation towards Image Super ResolutionCode0
Gated Fusion Network for Joint Image Deblurring and Super-ResolutionCode0
ML-CrAIST: Multi-scale Low-high Frequency Information-based Cross black Attention with Image Super-resolving TransformerCode0
Multi-level Wavelet-CNN for Image RestorationCode0
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
MaskBlur: Spatial and Angular Data Augmentation for Light Field Image Super-ResolutionCode0
Frequency Separation for Real-World Super-ResolutionCode0
Data-Free Knowledge Distillation for Image Super-ResolutionCode0
Maintaining Natural Image Statistics with the Contextual LossCode0
Data Upcycling Knowledge Distillation for Image Super-ResolutionCode0
Multi-level Wavelet Convolutional Neural NetworksCode0
Data-driven Super-Resolution of Flood Inundation Maps using Synthetic SimulationsCode0
Localized Super Resolution for Foreground Images using U-Net and MR-CNNCode0
Lightweight Image Super-Resolution with Adaptive Weighted Learning NetworkCode0
Lightweight and Efficient Image Super-Resolution with Block State-based Recursive NetworkCode0
BadRefSR: Backdoor Attacks Against Reference-based Image Super ResolutionCode0
Lightweight Feature Fusion Network for Single Image Super-ResolutionCode0
LossAgent: Towards Any Optimization Objectives for Image Processing with LLM AgentsCode0
CurvPnP: Plug-and-play Blind Image Restoration with Deep Curvature DenoiserCode0
Image-Adaptive GAN based ReconstructionCode0
Fine-grained Attention and Feature-sharing Generative Adversarial Networks for Single Image Super-ResolutionCode0
DACN: Dual-Attention Convolutional Network for Hyperspectral Image Super-ResolutionCode0
Learning Series-Parallel Lookup Tables for Efficient Image Super-ResolutionCode0
Progressive Image Deraining Networks: A Better and Simpler BaselineCode0
Learning Parallax Attention for Stereo Image Super-ResolutionCode0
Feedback Network for Image Super-ResolutionCode0
Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution ForestsCode0
Learning Multiple Probabilistic Degradation Generators for Unsupervised Real World Image Super ResolutionCode0
MAANet: Multi-view Aware Attention Networks for Image Super-ResolutionCode0
QuantNAS for super resolution: searching for efficient quantization-friendly architectures against quantization noiseCode0
Multimodal Image Super-resolution via Joint Sparse Representations induced by Coupled DictionariesCode0
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip ConnectionsCode0
Learning a No-Reference Quality Metric for Single-Image Super-ResolutionCode0
Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsCode0
FC^2N: Fully Channel-Concatenated Network for Single Image Super-ResolutionCode0
Fast Omni-Directional Image Super-Resolution: Adapting the Implicit Image Function with Pixel and Semantic-Wise Spherical Geometric PriorsCode0
Adaptation of the super resolution SOTA for Art Restoration in camera capture imagesCode0
Learning Accurate and Enriched Features for Stereo Image Super-ResolutionCode0
Fast Full-Resolution Target-Adaptive CNN-Based Pansharpening FrameworkCode0
Fast Bayesian Uncertainty Estimation and Reduction of Batch Normalized Single Image Super-Resolution NetworkCode0
LatticeNet: Towards Lightweight Image Super-resolution with Lattice BlockCode0
Fast and Robust Cascade Model for Multiple Degradation Single Image Super-ResolutionCode0
Fast and Efficient Image Quality Enhancement via Desubpixel Convolutional Neural NetworksCode0
Fast and Accurate Single Image Super-Resolution via Information Distillation NetworkCode0
LCSCNet: Linear Compressing Based Skip-Connecting Network for Image Super-ResolutionCode0
Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid NetworksCode0
LAP: a Linearize and Project Method for Solving Inverse Problems with Coupled VariablesCode0
Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in NetworkCode0
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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