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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1581–1590 of 3874 papers

TitleStatusHype
A Coordinate Descent Approach to Atomic Norm Denoising—0
2D Neural Fields with Learned Discontinuities—0
HypervolGAN: An efficient approach for GAN with multi-objective training function—0
Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation—0
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain—0
CRNet: Image Super-Resolution Using A Convolutional Sparse Coding Inspired Network—0
Hyperspectral Super-Resolution by Coupled Spectral Unmixing—0
Gridless Tomographic SAR Imaging Based on Accelerated Atomic Norm Minimization with Efficiency—0
Deep Attentive Generative Adversarial Network for Photo-Realistic Image De-Quantization—0
Fine-Grained Neural Architecture Search—0
Show:102550
← PrevPage 159 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified