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 2401–2425 of 3874 papers

TitleStatusHype
Image Enhancement by Recurrently-trained Super-resolution Network—0
Image inpainting for corrupted images by using the semi-super resolution GAN—0
Image Inpainting for High-Resolution Textures using CNN Texture Synthesis—0
Image Neural Field Diffusion Models—0
Imagen Video: High Definition Video Generation with Diffusion Models—0
ImagePairs: Realistic Super Resolution Dataset via Beam Splitter Camera Rig—0
Image Processing GNN: Breaking Rigidity in Super-Resolution—0
Image quality assessment for determining efficacy and limitations of Super-Resolution Convolutional Neural Network (SRCNN)—0
Image Reconstruction of Multi Branch Feature Multiplexing Fusion Network with Mixed Multi-layer Attention—0
Image Resolution Enhancement by Using Interpolation Followed by Iterative Back Projection—0
Image Restoration by Deep Projected GSURE—0
Progressive Image Super-Resolution via Neural Differential Equation—0
Image Restoration using Autoencoding Priors—0
Image Super-Resolution Based on Sparsity Prior via Smoothed l_0 Norm—0
Image super-resolution reconstruction based on attention mechanism and feature fusion—0
Image Super-resolution Reconstruction Network based on Enhanced Swin Transformer via Alternating Aggregation of Local-Global Features—0
Image Super-Resolution Using Attention Based DenseNet with Residual Deconvolution—0
Image Super-Resolution using Explicit Perceptual Loss—0
Image Superresolution using Scale-Recurrent Dense Network—0
Image Super-Resolution Using T-Tetromino Pixels—0
Image Super-Resolution Using VDSR-ResNeXt and SRCGAN—0
Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy—0
Perception-oriented Single Image Super-Resolution via Dual Relativistic Average Generative Adversarial Networks—0
Image Super-Resolution With Deep Variational Autoencoders—0
Image Super-Resolution with Guarantees via Conformalized Generative Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified