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 1291–1300 of 3874 papers

TitleStatusHype
Lightweight and Efficient Image Super-Resolution with Block State-based Recursive NetworkCode0
Lightweight Image Super-Resolution with Adaptive Weighted Learning NetworkCode0
ContrastiveGaussian: High-Fidelity 3D Generation with Contrastive Learning and Gaussian SplattingCode0
A Self-Supervised Deep Denoiser for Hyperspectral and Multispectral Image FusionCode0
Leveraging Segment Anything Model in Identifying Buildings within Refugee Camps (SAM4Refugee) from Satellite Imagery for Humanitarian OperationsCode0
ASDN: A Deep Convolutional Network for Arbitrary Scale Image Super-ResolutionCode0
Learning to Super Resolve Intensity Images from EventsCode0
Continual Learning Approaches for Anomaly DetectionCode0
Handheld Multi-Frame Super-ResolutionCode0
Learning Parallax Attention for Stereo Image Super-ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified