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 581–590 of 3874 papers

TitleStatusHype
Angular Super-Resolution in Diffusion MRI with a 3D Recurrent Convolutional AutoencoderCode1
Diffusion Models Beat GANs on Image ClassificationCode1
Deep learning techniques for blind image super-resolution: A high-scale multi-domain perspective evaluationCode1
DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-ResolutionCode1
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image SynthesisCode1
CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-ResolutionCode1
Discrete Cosine Transform Network for Guided Depth Map Super-ResolutionCode1
DiSR-NeRF: Diffusion-Guided View-Consistent Super-Resolution NeRFCode1
Deep Plug-and-Play Super-Resolution for Arbitrary Blur KernelsCode1
Deep Learning-Based Multiband Signal Fusion for 3-D SAR Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified