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 2481–2490 of 3874 papers

TitleStatusHype
Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate Networks—0
Scale-arbitrary Invertible Image Downscaling—0
Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images—0
Scale-Invariant Adversarial Attack against Arbitrary-scale Super-resolution—0
Scale-Transferrable Object Detection—0
SCAN-MUSIC: An Efficient Super-resolution Algorithm for Single Snapshot Wide-band Line Spectral Estimation—0
Scene Prior Filtering for Depth Super-Resolution—0
Scene Text Image Super-Resolution via Content Perceptual Loss and Criss-Cross Transformer Blocks—0
Screentone-Aware Manga Super-Resolution Using DeepLearning—0
SCSNet: An Efficient Paradigm for Learning Simultaneously Image Colorization and Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified