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 361–370 of 3874 papers

TitleStatusHype
Learning to Reconstruct Accelerated MRI Through K-space Cold Diffusion without NoiseCode1
Scene Text Image Super-resolution based on Text-conditional Diffusion ModelsCode1
A Spectral Diffusion Prior for Hyperspectral Image Super-ResolutionCode1
A Lightweight Recurrent Aggregation Network for Satellite Video Super-ResolutionCode1
Lightweight super resolution network for point cloud geometry compressionCode1
VCISR: Blind Single Image Super-Resolution with Video Compression Synthetic DataCode1
Optimal Transport-Guided Conditional Score-Based Diffusion ModelsCode1
EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-ResolutionCode1
Efficient Test-Time Adaptation for Super-Resolution with Second-Order Degradation and ReconstructionCode1
INCODE: Implicit Neural Conditioning with Prior Knowledge EmbeddingsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified