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 821–830 of 3874 papers

TitleStatusHype
Designing a Practical Degradation Model for Deep Blind Image Super-ResolutionCode1
Asymmetric CNN for image super-resolutionCode1
UltraSR: Spatial Encoding is a Missing Key for Implicit Image Function-based Arbitrary-Scale Super-ResolutionCode1
MONAIfbs: MONAI-based fetal brain MRI deep learning segmentationCode1
A new public Alsat-2B dataset for single-image super-resolutionCode1
Self-Supervised Adaptation for Video Super-ResolutionCode1
Collapsible Linear Blocks for Super-Efficient Super ResolutionCode1
3D Human Pose, Shape and Texture from Low-Resolution Images and VideosCode1
ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data CharacteristicCode1
Generating Images with Sparse RepresentationsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified