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 1791–1800 of 3874 papers

TitleStatusHype
Image Super-Resolution Using T-Tetromino Pixels—0
Image Superresolution using Scale-Recurrent Dense Network—0
Deep Photo Cropper and Enhancer—0
Image Super-Resolution using Explicit Perceptual Loss—0
Deep Nonparametric Convexified Filtering for Computational Photography, Image Synthesis and Adversarial Defense—0
Better to Follow, Follow to Be Better: Towards Precise Supervision of Feature Super-Resolution for Small Object Detection—0
Image Super-Resolution Using Attention Based DenseNet with Residual Deconvolution—0
Image Super-resolution Reconstruction Network based on Enhanced Swin Transformer via Alternating Aggregation of Local-Global Features—0
Deep Neural Network for Fast and Accurate Single Image Super-Resolution via Channel-Attention-based Fusion of Orientation-aware Features—0
Image super-resolution reconstruction based on attention mechanism and feature fusion—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified