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 1081–1090 of 3874 papers

TitleStatusHype
DiffuseIR:Diffusion Models For Isotropic Reconstruction of 3D Microscopic Images—0
Boomerang: Local sampling on image manifolds using diffusion models—0
DiffStereo: High-Frequency Aware Diffusion Model for Stereo Image Restoration—0
DIffSteISR: Harnessing Diffusion Prior for Superior Real-world Stereo Image Super-Resolution—0
γ-Net: Superresolving SAR Tomographic Inversion via Deep Learning—0
An Approach to Super-Resolution of Sentinel-2 Images Based on Generative Adversarial Networks—0
Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network—0
DiffSSC: Semantic LiDAR Scan Completion using Denoising Diffusion Probabilistic Models—0
BOLD: Boolean Logic Deep Learning—0
An approach to image denoising using manifold approximation without clean images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified