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 231–240 of 3874 papers

TitleStatusHype
Deep Posterior Distribution-based Embedding for Hyperspectral Image Super-resolutionCode1
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion ModelsCode1
2-Step Sparse-View CT Reconstruction with a Domain-Specific Perceptual NetworkCode1
Deep Blind Video Super-resolutionCode1
A new public Alsat-2B dataset for single-image super-resolutionCode1
Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-ResolutionCode1
Aligned Structured Sparsity Learning for Efficient Image Super-ResolutionCode1
Deep Face Super-Resolution with Iterative Collaboration between Attentive Recovery and Landmark EstimationCode1
Adaptive Patch Exiting for Scalable Single Image Super-ResolutionCode1
2DeteCT -- A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learningCode1
Show:102550
← PrevPage 24 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified