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 851–900 of 1589 papers

TitleStatusHype
Geometric Distortion Guided Transformer for Omnidirectional Image Super-Resolution—0
Geometry-Aware Neighborhood Search for Learning Local Models for Image Reconstruction—0
Geometry-Aware Reference Synthesis for Multi-View Image Super-Resolution—0
GHM Wavelet Transform for Deep Image Super Resolution—0
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP—0
GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution—0
Global and Local Mamba Network for Multi-Modality Medical Image Super-Resolution—0
GRAN: Ghost Residual Attention Network for Single Image Super Resolution—0
GuideSR: Rethinking Guidance for One-Step High-Fidelity Diffusion-Based Super-Resolution—0
GUN: Gradual Upsampling Network for Single Image Super-Resolution—0
HAAT: Hybrid Attention Aggregation Transformer for Image Super-Resolution—0
Hierarchical Information Flow for Generalized Efficient Image Restoration—0
Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution—0
Higher-order MRFs based image super resolution: why not MAP?—0
High-Frequency aware Perceptual Image Enhancement—0
High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network—0
High Resolution 3D Shape Texture from Multiple Videos—0
High-Similarity-Pass Attention for Single Image Super-Resolution—0
HIIF: Hierarchical Encoding based Implicit Image Function for Continuous Super-resolution—0
Hi-Mamba: Hierarchical Mamba for Efficient Image Super-Resolution—0
HIME: Efficient Headshot Image Super-Resolution with Multiple Exemplars—0
HIPA: Hierarchical Patch Transformer for Single Image Super Resolution—0
HiREN: Towards Higher Supervision Quality for Better Scene Text Image Super-Resolution—0
Histo-Diffusion: A Diffusion Super-Resolution Method for Digital Pathology with Comprehensive Quality Assessment—0
HiT-SR: Hierarchical Transformer for Efficient Image Super-Resolution—0
How Real is Real: Evaluating the Robustness of Real-World Super Resolution—0
HSR-Diff:Hyperspectral Image Super-Resolution via Conditional Diffusion Models—0
HSR-Diff: Hyperspectral Image Super-Resolution via Conditional Diffusion Models—0
Hybrid Transformer and CNN Attention Network for Stereo Image Super-resolution—0
HyperSound: Generating Implicit Neural Representations of Audio Signals with Hypernetworks—0
Hyperspectral Image Super-Resolution in Arbitrary Input-Output Band Settings—0
Hyperspectral Image Super-resolution via Deep Spatio-spectral Convolutional Neural Networks—0
Hyperspectral Image Super-Resolution via Dual-domain Network Based on Hybrid Convolution—0
Hyperspectral Image Super-Resolution via Non-Local Sparse Tensor Factorization—0
HypervolGAN: An efficient approach for GAN with multi-objective training function—0
ICF-SRSR: Invertible scale-Conditional Function for Self-Supervised Real-world Single Image Super-Resolution—0
Image Denoising and Super-Resolution using Residual Learning of Deep Convolutional Network—0
Image Inpainting for High-Resolution Textures using CNN Texture Synthesis—0
ImagePairs: Realistic Super Resolution Dataset via Beam Splitter Camera Rig—0
Image Processing GNN: Breaking Rigidity in Super-Resolution—0
Image Reconstruction of Multi Branch Feature Multiplexing Fusion Network with Mixed Multi-layer Attention—0
Image Resolution Enhancement by Using Interpolation Followed by Iterative Back Projection—0
Progressive Image Super-Resolution via Neural Differential Equation—0
Image Super-Resolution Based on Sparsity Prior via Smoothed l_0 Norm—0
Image super-resolution reconstruction based on attention mechanism and feature fusion—0
Image Super-resolution Reconstruction Network based on Enhanced Swin Transformer via Alternating Aggregation of Local-Global Features—0
Image Super-Resolution Using Attention Based DenseNet with Residual Deconvolution—0
Image Super-Resolution using Explicit Perceptual Loss—0
Image Superresolution using Scale-Recurrent Dense Network—0
Image Super-Resolution Using T-Tetromino Pixels—0
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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