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 3091–3100 of 3874 papers

TitleStatusHype
Optimal Surface Segmentation with Convex Priors in Irregularly Sampled Space—0
Optimal Transport driven CycleGAN for Unsupervised Learning in Inverse Problems—0
Optimal Transport for Super Resolution Applied to Astronomy Imaging—0
Optimization of Module Transferability in Single Image Super-Resolution: Universality Assessment and Cycle Residual Blocks—0
Optimizing Drug Delivery in Smart Pharmacies: A Novel Framework of Multi-Stage Grasping Network Combined with Adaptive Robotics Mechanism—0
Optimizing Fingerprint-Spectrum-Based Synchronization in Integrated Sensing and Communications—0
Optimizing Generative Adversarial Networks for Image Super Resolution via Latent Space Regularization—0
Optimizing Skin Lesion Classification via Multimodal Data and Auxiliary Task Integration—0
ORL-LDM: Offline Reinforcement Learning Guided Latent Diffusion Model Super-Resolution Reconstruction—0
Orthogonally Regularized Deep Networks For Image Super-resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified