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 251–260 of 3874 papers

TitleStatusHype
Aligned Structured Sparsity Learning for Efficient Image Super-ResolutionCode1
Adaptive Patch Exiting for Scalable Single Image Super-ResolutionCode1
2DeteCT -- A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learningCode1
A Lightweight Recurrent Aggregation Network for Satellite Video Super-ResolutionCode1
Decoupled Data Consistency with Diffusion Purification for Image RestorationCode1
Simultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with Analytical Image AttenuationCode1
DeblurSR: Event-Based Motion Deblurring Under the Spiking RepresentationCode1
Accelerating Diffusion Models for Inverse Problems through Shortcut SamplingCode1
Decomposition-Based Variational Network for Multi-Contrast MRI Super-Resolution and ReconstructionCode1
DDet: Dual-path Dynamic Enhancement Network for Real-World Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified