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 1101–1110 of 3874 papers

TitleStatusHype
Block-Based Multi-Scale Image Rescaling—0
Differentiable Channel Sparsity Search via Weight Sharing within Filters—0
Diff-Ensembler: Learning to Ensemble 2D Diffusion Models for Volume-to-Volume Medical Image Translation—0
Blind Time-of-Flight Imaging: Sparse Deconvolution on the Continuum with Unknown Kernels—0
AdaWaveNet: Adaptive Wavelet Network for Time Series Analysis—0
DiffDance: Cascaded Human Motion Diffusion Model for Dance Generation—0
Analyzing an Imitation Learning Network for Fundus Image Registration Using a Divide-and-Conquer Approach—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
Accurate Lung Nodules Segmentation with Detailed Representation Transfer and Soft Mask Supervision—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified