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 3791–3800 of 3874 papers

TitleStatusHype
Geometric Distortion Guided Transformer for Omnidirectional Image Super-Resolution—0
ADIR: Adaptive Diffusion for Image Reconstruction—0
Geometry-Aware Neighborhood Search for Learning Local Models for Image Reconstruction—0
Geometry-Aware Reference Synthesis for Multi-View Image Super-Resolution—0
Geometry Enhancements from Visual Content: Going Beyond Ground Truth—0
Dynamic Snake Upsampling Operater and Boundary-Skeleton Weighted Loss for Tubular Structure Segmentation—0
GHM Wavelet Transform for Deep Image Super Resolution—0
A Deep Residual Star Generative Adversarial Network for multi-domain Image Super-Resolution—0
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP—0
GITO: Graph-Informed Transformer Operator for Learning Complex Partial Differential Equations—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified