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 951975 of 1589 papers

TitleStatusHype
DA-VSR: Domain Adaptable Volumetric Super-Resolution For Medical Images0
MuS2: A Real-World Benchmark for Sentinel-2 Multi-Image Super-ResolutionCode0
Effective Invertible Arbitrary Image Rescaling0
Recurrent Super-Resolution Method for Enhancing Low Quality Thermal Facial Data0
MMSR: Multiple-Model Learned Image Super-Resolution Benefiting From Class-Specific Image Priors0
Lightweight Spatial-Channel Adaptive Coordination of Multilevel Refinement Enhancement Network for Image Reconstruction0
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 Concatenation0
Time-lapse image classification using a diffractive neural network0
Towards Robust Drone Vision in the Wild0
Latent Multi-Relation Reasoning for GAN-Prior based Image Super-Resolution0
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 Reconstruction0
Sub-Aperture Feature Adaptation in Single Image Super-resolution Model for Light Field Imaging0
Learning Generalizable Latent Representations for Novel Degradations in Super Resolution0
Improved Super Resolution of MR Images Using CNNs and Vision Transformers0
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-Resolution0
Learning Knowledge Representation with Meta Knowledge Distillation for Single Image Super-Resolution0
E2FIF: Push the limit of Binarized Deep Imagery Super-resolution using End-to-end Full-precision Information FlowCode0
Perception-Oriented Stereo Image Super-Resolution0
Rethinking Super-Resolution as Text-Guided Details Generation0
Exploring the solution space of linear inverse problems with GAN latent geometry0
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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
8SwinFIRPSNR29.36Unverified
9CPAT+PSNR29.36Unverified
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