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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1451–1475 of 1589 papers

TitleStatusHype
Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution ForestsCode0
Image Transformer—0
Deep Image Super Resolution via Natural Image Priors—0
Orthogonally Regularized Deep Networks For Image Super-resolution—0
SESR: Single Image Super Resolution with Recursive Squeeze and Excitation NetworksCode0
Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks—0
High-throughput, high-resolution registration-free generated adversarial network microscopyCode0
Super-Resolution with Deep Adaptive Image Resampling—0
"Zero-Shot" Super-Resolution using Deep Internal LearningCode0
Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsCode0
SRPGAN: Perceptual Generative Adversarial Network for Single Image Super Resolution—0
Deep Burst DenoisingCode0
Image Super-resolution via Feature-augmented Random ForestCode0
A Frequency Domain Neural Network for Fast Image Super-resolution—0
Image Inpainting for High-Resolution Textures using CNN Texture Synthesis—0
Super-FAN: Integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with GANs—0
Deep Sampling Networks—0
BLADE: Filter Learning for General Purpose Computational Photography—0
Super-Resolution for Overhead Imagery Using DenseNets and Adversarial Learning—0
Deep Inception-Residual Laplacian Pyramid Networks for Accurate Single Image Super-Resolution—0
CT-SRCNN: Cascade Trained and Trimmed Deep Convolutional Neural Networks for Image Super Resolution—0
Single Image Super-Resolution Using Lightweight CNN with Maxout Units—0
Generative Adversarial Networks: An OverviewCode0
Retinal Vasculature Segmentation Using Local Saliency Maps and Generative Adversarial Networks For Image Super Resolution—0
Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid NetworksCode0
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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