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 3021–3030 of 3874 papers

TitleStatusHype
PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration—0
An Image Analogies Approach for Multi-Scale Contour Detection—0
Sequential Hierarchical Learning with Distribution Transformation for Image Super-Resolution—0
Super-Resolution Remote Imaging using Time Encoded Remote Apertures—0
Learning with Privileged Information for Efficient Image Super-Resolution—0
EAGLE: Large-scale Vehicle Detection Dataset in Real-World Scenarios using Aerial Imagery—0
Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution—0
Feedback Neural Network based Super-resolution of DEM for generating high fidelity features—0
A deep primal-dual proximal network for image restoration—0
Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified