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 3201–3210 of 3874 papers

TitleStatusHype
Deeply Matting-based Dual Generative Adversarial Network for Image and Document Label Supervision—0
Multi-FAN: Multi-Spectral Mosaic Super-Resolution Via Multi-Scale Feature Aggregation Network—0
TextSR: Content-Aware Text Super-Resolution Guided by RecognitionCode1
Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator—0
Phase Retrieval using Untrained Neural Network Priors—0
Blind Super-Resolution Kernel Estimation using an Internal-GANCode0
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise ModulationsCode0
Edge-Informed Single Image Super-ResolutionCode0
Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors—0
JSI-GAN: GAN-Based Joint Super-Resolution and Inverse Tone-Mapping with Pixel-Wise Task-Specific Filters for UHD HDR VideoCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified