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 1661–1670 of 3874 papers

TitleStatusHype
HR Human: Modeling Human Avatars with Triangular Mesh and High-Resolution Textures from Videos—0
AdaWaveNet: Adaptive Wavelet Network for Time Series Analysis—0
Infrared Image Super-Resolution via Lightweight Information Split Network—0
CoReGAN: Contrastive Regularized Generative Adversarial Network for Guided Depth Map Super Resolution—0
Frequency-Domain Refinement with Multiscale Diffusion for Super Resolution—0
RGB Guided ToF Imaging System: A Survey of Deep Learning-based Methods—0
Perception- and Fidelity-aware Reduced-Reference Super-Resolution Image Quality Assessment—0
Large coordinate kernel attention network for lightweight image super-resolution—0
NAFRSSR: a Lightweight Recursive Network for Efficient Stereo Image Super-ResolutionCode0
Super-Resolving Blurry Images with Events—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified