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 441–450 of 3874 papers

TitleStatusHype
DeepBedMap: Using a deep neural network to better resolve the bed topography of AntarcticaCode1
Best-Buddy GANs for Highly Detailed Image Super-ResolutionCode1
Aligned Structured Sparsity Learning for Efficient Image Super-ResolutionCode1
Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-ResolutionCode1
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion ModelsCode1
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image DenoisingCode1
Accelerating Guided Diffusion Sampling with Splitting Numerical MethodsCode1
Deep Blind Super-Resolution for Satellite VideoCode1
Deep Audio Waveform PriorCode1
Deep Arbitrary-Scale Image Super-Resolution via Scale-Equivariance PursuitCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified