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 3131–3140 of 3874 papers

TitleStatusHype
Performance Comparison of Convolutional AutoEncoders, Generative Adversarial Networks and Super-Resolution for Image Compression—0
PFStorer: Personalized Face Restoration and Super-Resolution—0
PG-DPIR: An efficient plug-and-play method for high-count Poisson-Gaussian inverse problems—0
PH2ST:ST-Prompt Guided Histological Hypergraph Learning for Spatial Gene Expression Prediction—0
Phase Retrieval using Untrained Neural Network Priors—0
Photon-counting CT using a Conditional Diffusion Model for Super-resolution and Texture-preservation—0
Photorealistic Video Generation with Diffusion Models—0
Perceptual Video Super Resolution with Enhanced Temporal Consistency—0
Photothermal-SR-Net: A Customized Deep Unfolding Neural Network for Photothermal Super Resolution Imaging—0
Physics-informed deep-learning applications to experimental fluid mechanics—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified