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 951–1000 of 1589 papers

TitleStatusHype
DA-VSR: Domain Adaptable Volumetric Super-Resolution For Medical Images—0
MuS2: A Real-World Benchmark for Sentinel-2 Multi-Image Super-ResolutionCode0
Effective Invertible Arbitrary Image Rescaling—0
Recurrent Super-Resolution Method for Enhancing Low Quality Thermal Facial Data—0
MMSR: Multiple-Model Learned Image Super-Resolution Benefiting From Class-Specific Image Priors—0
Lightweight Spatial-Channel Adaptive Coordination of Multilevel Refinement Enhancement Network for Image Reconstruction—0
QuantNAS for super resolution: searching for efficient quantization-friendly architectures against quantization noiseCode0
XCAT -- Lightweight Quantized Single Image Super-Resolution using Heterogeneous Group Convolutions and Cross Concatenation—0
Time-lapse image classification using a diffractive neural network—0
Towards Robust Drone Vision in the Wild—0
Latent Multi-Relation Reasoning for GAN-Prior based Image Super-Resolution—0
Criteria Comparative Learning for Real-scene Image Super-ResolutionCode0
Learning Series-Parallel Lookup Tables for Efficient Image Super-ResolutionCode0
Sparse-based Domain Adaptation Network for OCTA Image Super-Resolution Reconstruction—0
Sub-Aperture Feature Adaptation in Single Image Super-resolution Model for Light Field Imaging—0
Learning Generalizable Latent Representations for Novel Degradations in Super Resolution—0
Improved Super Resolution of MR Images Using CNNs and Vision Transformers—0
Enhancing Image Rescaling using Dual Latent Variables in Invertible Neural NetworkCode0
Semantic uncertainty intervals for disentangled latent spacesCode0
Geometry-Aware Reference Synthesis for Multi-View Image Super-Resolution—0
Learning Knowledge Representation with Meta Knowledge Distillation for Single Image Super-Resolution—0
E2FIF: Push the limit of Binarized Deep Imagery Super-resolution using End-to-end Full-precision Information FlowCode0
Perception-Oriented Stereo Image Super-Resolution—0
Rethinking Super-Resolution as Text-Guided Details Generation—0
Exploring the solution space of linear inverse problems with GAN latent geometry—0
PhySRNet: Physics informed super-resolution network for application in computational solid mechanics—0
Bilateral Network with Channel Splitting Network and Transformer for Thermal Image Super-Resolution—0
Multi-Modality Image Super-Resolution using Generative Adversarial NetworksCode0
Real-World Single Image Super-Resolution Under Rainy Condition—0
A No-Reference Deep Learning Quality Assessment Method for Super-resolution Images Based on Frequency Maps—0
Robust Deep Ensemble Method for Real-world Image DenoisingCode0
Patch-based image Super Resolution using generalized Gaussian mixture model—0
Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution—0
Real-Time Super-Resolution for Real-World Images on Mobile Devices—0
Super-resolving 2D stress tensor field conserving equilibrium constraints using physics informed U-Net—0
Efficient Multi-Purpose Cross-Attention Based Image Alignment Block for Edge Devices—0
Image Reconstruction of Multi Branch Feature Multiplexing Fusion Network with Mixed Multi-layer Attention—0
Textural-Perceptual Joint Learning for No-Reference Super-Resolution Image Quality AssessmentCode0
Cross-domain heterogeneous residual network for single image super-resolutionCode0
Diverse super-resolution with pretrained deep hiererarchical VAEs—0
A Comparative Study of Feature Expansion Unit for 3D Point Cloud Upsampling—0
Semantically Accurate Super-Resolution Generative Adversarial Networks—0
SPQE: Structure-and-Perception-Based Quality Evaluation for Image Super-Resolution—0
Generative Adversarial Networks for Image Super-Resolution: A Survey—0
IMDeception: Grouped Information Distilling Super-Resolution Network—0
NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results—0
GHM Wavelet Transform for Deep Image Super Resolution—0
Super-resolved multi-temporal segmentation with deep permutation-invariant networks—0
Tracking Urbanization in Developing Regions with Remote Sensing Spatial-Temporal Super-Resolution—0
Single Image Internal Distribution Measurement Using Non-Local Variational Autoencoder—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