SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2961–2970 of 3874 papers

TitleStatusHype
Deep Interleaved Network for Image Super-Resolution With Asymmetric Co-AttentionCode1
Mining self-similarity: Label super-resolution with epitomic representationsCode0
SimUSR: A Simple but Strong Baseline for Unsupervised Image Super-resolution—0
Towards Real-Time DNN Inference on Mobile Platforms with Model Pruning and Compiler Optimization—0
Microscopy Image Restoration using Deep Learning on W2SCode1
Single Pair Cross-Modality Super Resolution—0
ImagePairs: Realistic Super Resolution Dataset via Beam Splitter Camera Rig—0
Super-Resolution-based Snake Model -- An Unsupervised Method for Large-Scale Building Extraction using Airborne LiDAR Data and Optical ImageCode1
DeepBedMap: Using a deep neural network to better resolve the bed topography of AntarcticaCode1
Unified Dynamic Convolutional Network for Super-Resolution with Variational Degradations—0
Show:102550
← PrevPage 297 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified