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 1976–2000 of 3874 papers

TitleStatusHype
Deep Neural Network-based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions—0
LapGSR: Laplacian Reconstructive Network for Guided Thermal Super-Resolution—0
Zoom in to the details of human-centric videos—0
Large coordinate kernel attention network for lightweight image super-resolution—0
Large Hole Image Inpainting With Compress-Decompression Network—0
Deep Networks for Image Super-Resolution with Sparse Prior—0
Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling—0
Large Receptive Field Networks for High-Scale Image Super-Resolution—0
Large-scale single-photon imaging—0
Towards Realistic Data Generation for Real-World Super-Resolution—0
LASSR: Effective Super-Resolution Method for Plant Disease Diagnosis—0
Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image Super-Resolution—0
Deep Networks for Image and Video Super-Resolution—0
Latent Multi-Relation Reasoning for GAN-Prior based Image Super-Resolution—0
Latent-Shift: Latent Diffusion with Temporal Shift for Efficient Text-to-Video Generation—0
Deep multi-frame face super-resolution—0
LATIS: Lambda Abstraction-based Thermal Image Super-resolution—0
Adaptive Loss Function for Super Resolution Neural Networks Using Convex Optimization Techniques—0
LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-Resolution—0
Deep MR Image Super-Resolution Using Structural Priors—0
Layered Diffusion Model for One-Shot High Resolution Text-to-Image Synthesis—0
Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network—0
Towards Real-Time DNN Inference on Mobile Platforms with Model Pruning and Compiler Optimization—0
Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors—0
Learnable Sampling 3D Convolution for Video Enhancement and Action Recognition—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified