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 1211–1220 of 3874 papers

TitleStatusHype
A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective—0
Deep Slice Interpolation via Marginal Super-Resolution, Fusion and Refinement—0
Deep Selective Combinatorial Embedding and Consistency Regularization for Light Field Super-resolution—0
A Low-Resolution Image is Worth 1x1 Words: Enabling Fine Image Super-Resolution with Transformers and TaylorShift—0
DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution—0
Deep Sampling Networks—0
Beyond Principal Components: Deep Boltzmann Machines for Face Modeling—0
Deep Residual Networks with a Fully Connected Recon-struction Layer for Single Image Super-Resolution—0
All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation—0
DRCAS: Deep Restoration Network for Hardware Based Compressive Acquisition Scheme—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified