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 751775 of 1589 papers

TitleStatusHype
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
Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid NetworksCode0
Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in NetworkCode0
Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual NetworkCode0
A Fusion-Guided Inception Network for Hyperspectral Image Super-ResolutionCode0
Image Super-Resolution via Dual-State Recurrent NetworksCode0
Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsCode0
Cross-domain heterogeneous residual network for single image super-resolutionCode0
Learning a No-Reference Quality Metric for Single-Image Super-ResolutionCode0
A Fully Progressive Approach to Single-Image Super-ResolutionCode0
Attention-based Multi-Reference Learning for Image Super-ResolutionCode0
Learning Accurate and Enriched Features for Stereo Image Super-ResolutionCode0
EXTRACTER: Efficient Texture Matching with Attention and Gradient Enhancing for Large Scale Image Super ResolutionCode0
Criteria Comparative Learning for Real-scene Image Super-ResolutionCode0
Correction Filter for Single Image Super-Resolution: Robustifying Off-the-Shelf Deep Super-ResolversCode0
Exploring Linear Attention Alternative for Single Image Super-ResolutionCode0
LAR-SR: A Local Autoregressive Model for Image Super-ResolutionCode0
Explorable Super ResolutionCode0
LAP: a Linearize and Project Method for Solving Inverse Problems with Coupled VariablesCode0
LatticeNet: Towards Lightweight Image Super-resolution with Lattice BlockCode0
Exemplar Guided Face Image Super-Resolution without Facial LandmarksCode0
Joint Maximum Purity Forest with Application to Image Super-ResolutionCode0
Kernel Modeling Super-Resolution on Real Low-Resolution ImagesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR29.54Unverified
2HMA†PSNR29.51Unverified
3Hi-IR-LPSNR29.49Unverified
4HAT-LPSNR29.47Unverified
5HAT_FIRPSNR29.44Unverified
6DRCTPSNR29.4Unverified
7HATPSNR29.38Unverified
8CPAT+PSNR29.36Unverified
9SwinFIRPSNR29.36Unverified
10CPATPSNR29.34Unverified
#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR28.16Unverified
2HMA†PSNR28.13Unverified
3Hi-IR-LPSNR28.13Unverified
4HAT-LPSNR28.09Unverified
5HAT_FIRPSNR28.07Unverified
6CPAT+PSNR28.06Unverified
7DRCTPSNR28.06Unverified
8HATPSNR28.05Unverified
9CPATPSNR28.04Unverified
10SwinFIRPSNR28.03Unverified
#ModelMetricClaimedVerifiedStatus
1Hi-IR-LPSNR28.72Unverified
2DRCT-LPSNR28.7Unverified
3HMA†PSNR28.69Unverified
4HAT-LPSNR28.6Unverified
5HAT_FIRPSNR28.43Unverified
6DRCTPSNR28.4Unverified
7HATPSNR28.37Unverified
8CPAT+PSNR28.33Unverified
9CPATPSNR28.22Unverified
10PFTPSNR28.2Unverified