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 1971–1980 of 3874 papers

TitleStatusHype
Label Super Resolution with Inter-Instance Loss—0
LAConv: Local Adaptive Convolution for Image Fusion—0
LaMAR: Laplacian Pyramid for Multimodal Adaptive Super Resolution (Student Abstract)—0
Language Independent Single Document Image Super-Resolution using CNN for improved recognition—0
Adaptive Segmentation-Based Initialization for Steered Mixture of Experts Image Regression—0
Deep Neural Network-based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions—0
LapGSR: Laplacian Reconstructive Network for Guided Thermal Super-Resolution—0
Zoom in to the details of human-centric videos—0
Large coordinate kernel attention network for lightweight image super-resolution—0
Large Hole Image Inpainting With Compress-Decompression Network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified