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 1751–1760 of 3874 papers

TitleStatusHype
Fast Spatio-Temporal Residual Network for Video Super-Resolution—0
Benchmarking Super-Resolution Algorithms on Real Data—0
Image inpainting for corrupted images by using the semi-super resolution GAN—0
Image Inpainting for High-Resolution Textures using CNN Texture Synthesis—0
Image Neural Field Diffusion Models—0
Imagen Video: High Definition Video Generation with Diffusion Models—0
ImagePairs: Realistic Super Resolution Dataset via Beam Splitter Camera Rig—0
Image Processing GNN: Breaking Rigidity in Super-Resolution—0
Fast single image super-resolution based on sigmoid transformation—0
Convolutional neural network based on sparse graph attention mechanism for MRI super-resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified