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 1131–1140 of 3874 papers

TitleStatusHype
Exploring the solution space of linear inverse problems with GAN latent geometry—0
Detail-Enhancing Framework for Reference-Based Image Super-Resolution—0
Detailed 3D Human Body Reconstruction from Multi-view Images Combining Voxel Super-Resolution and Learned Implicit Representation—0
Blind Image Super-Resolution via Contrastive Representation Learning—0
Blind Image Super-Resolution: A Survey and Beyond—0
Designing A Composite Dictionary Adaptively From Joint Examples—0
DepthwiseGANs: Fast Training Generative Adversarial Networks for Realistic Image Synthesis—0
Depth Super-Resolution from Explicit and Implicit High-Frequency Features—0
Analog Neural Computing with Super-resolution Memristor Crossbars—0
An Adversarial Super-Resolution Remedy for Radar Design Trade-offs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified