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 2141–2150 of 3874 papers

TitleStatusHype
XAI-based gait analysis of patients walking with Knee-Ankle-Foot orthosis using video cameras—0
DDS2M: Self-Supervised Denoising Diffusion Spatio-Spectral Model for Hyperspectral Image Restoration—0
DC-VSR: Spatially and Temporally Consistent Video Super-Resolution with Video Diffusion Prior—0
DCS-RISR: Dynamic Channel Splitting for Efficient Real-world Image Super-Resolution—0
DCIL: Deep Contextual Internal Learning for Image Restoration and Image Retargeting—0
Lightweight Image Super-Resolution with Multi-scale Feature Interaction Network—0
DA-VSR: Domain Adaptable Volumetric Super-Resolution For Medical Images—0
Lightweight single-image super-resolution network based on dual paths—0
Data-Driven Design for Fourier Ptychographic Microscopy—0
Lightweight Spatial-Channel Adaptive Coordination of Multilevel Refinement Enhancement Network for Image Reconstruction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified