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 2161–2170 of 3874 papers

TitleStatusHype
Enhancement or Super-Resolution: Learning-based Adaptive Video Streaming with Client-Side Video Processing—0
Virtual Coil Augmentation Technology for MR Coil Extrapolation via Deep Learning—0
Self-Supervised Deep Blind Video Super-ResolutionCode1
Improving Clinical Diagnosis Performance with Automated X-ray Scan Quality Enhancement Algorithms—0
Dual Perceptual Loss for Single Image Super-Resolution Using ESRGAN—0
CISRNet: Compressed Image Super-Resolution NetworkCode0
UDC: Unified DNAS for Compressible TinyML Models—0
SDT-DCSCN for Simultaneous Super-Resolution and Deblurring of Text ImagesCode0
Flexible Style Image Super-Resolution using Conditional ObjectiveCode1
Coarse-to-Fine Embedded PatchMatch and Multi-Scale Dynamic Aggregation for Reference-based Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified