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 391–400 of 3874 papers

TitleStatusHype
AdaDM: Enabling Normalization for Image Super-ResolutionCode1
Deep Blind Video Super-resolutionCode1
EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-ResolutionCode1
EDPN: Enhanced Deep Pyramid Network for Blurry Image RestorationCode1
Deep Image PriorCode1
AdaPool: Exponential Adaptive Pooling for Information-Retaining DownsamplingCode1
A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuningCode1
Simultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with Analytical Image AttenuationCode1
DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable DiffusionCode1
A-ESRGAN: Training Real-World Blind Super-Resolution with Attention U-Net DiscriminatorsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified