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 726750 of 1589 papers

TitleStatusHype
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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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