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

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Showing 1151–1160 of 3874 papers

TitleStatusHype
Decoupling Multi-Contrast Super-Resolution: Pairing Unpaired Synthesis with Implicit Representations—0
Joint Super-Resolution and Segmentation for 1-m Impervious Surface Area Mapping in China's Yangtze River Economic Belt—0
EAM: Enhancing Anything with Diffusion Transformers for Blind Super-Resolution—0
StereoINR: Cross-View Geometry Consistent Stereo Super Resolution with Implicit Neural Representation—0
Deep Learning Framework for Infrastructure Maintenance: Crack Detection and High-Resolution Imaging of Infrastructure Surfaces—0
USF Spectral Estimation: Prevalence of Gaussian Cramér-Rao Bounds Despite Modulo Folding—0
A Fusion-Guided Inception Network for Hyperspectral Image Super-ResolutionCode0
Optimization of Module Transferability in Single Image Super-Resolution: Universality Assessment and Cycle Residual Blocks—0
MSFNet-CPD: Multi-Scale Cross-Modal Fusion Network for Crop Pest DetectionCode0
Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified