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 2281–2290 of 3874 papers

TitleStatusHype
Generative Powers of Ten—0
Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis—0
Generator From Edges: Reconstruction of Facial Images—0
Generic 3D Convolutional Fusion for image restoration—0
Generic Perceptual Loss for Modeling Structured Output Dependencies—0
Geometric Distortion Guided Transformer for Omnidirectional Image Super-Resolution—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
GHM Wavelet Transform for Deep Image Super Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified