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 1681–1690 of 3874 papers

TitleStatusHype
Blind Time-of-Flight Imaging: Sparse Deconvolution on the Continuum with Unknown Kernels—0
DiffDance: Cascaded Human Motion Diffusion Model for Dance Generation—0
DifAugGAN: A Practical Diffusion-style Data Augmentation for GAN-based Single Image Super-resolution—0
Diagnosing and Preventing Instabilities in Recurrent Video Processing—0
Analyzing an Imitation Learning Network for Fundus Image Registration Using a Divide-and-Conquer Approach—0
AdaWaveNet: Adaptive Wavelet Network for Time Series Analysis—0
Multimodal Super-Resolution: Discovering hidden physics and its application to fusion plasmas—0
Diabetic foot ulcers monitoring by employing super resolution and noise reduction deep learning techniques—0
DHECA-SuperGaze: Dual Head-Eye Cross-Attention and Super-Resolution for Unconstrained Gaze Estimation—0
Blind Super-Resolution for Remote Sensing Images via Conditional Stochastic Normalizing Flows—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified