Multi-frame super resolution via deep plug-and-play CNN regularization

DOI10.1515/JIIP-2019-0054zbMath1458.94065OpenAlexW3029535673MaRDI QIDQ2194641

Shengrong Zhao, Hu Liang

Publication date: 4 September 2020

Published in: Journal of Inverse and Ill-Posed Problems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1515/jiip-2019-0054

zbMATH Keywords

regularizationplug-and-playsuper resolutionconvolutional neural network

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Uses Software

• Adam
• DnCNN

Cites Work

• Nonlinear total variation based noise removal algorithms
• The Little Engine that Could: Regularization by Denoising (RED)
• A new multiframe super-resolution based on nonlinear registration and a spatially weighted regularization
• Multi-frame image super-resolution reconstruction based on spatial information weighted fields of experts
• Super-Resolution of Multi-Observed RGB-D Images Based on Nonlocal Regression and Total Variation
• Robust Single Image Super-Resolution via Deep Networks With Sparse Prior
• Robust Single-Image Super-Resolution Based on Adaptive Edge-Preserving Smoothing Regularization
• A MAP-Based Approach for Hyperspectral Imagery Super-Resolution
• Robust Multi-Frame Super-Resolution Based on Spatially Weighted Half-Quadratic Estimation and Adaptive BTV Regularization
• A Convergent Image Fusion Algorithm Using Scene-Adapted Gaussian-Mixture-Based Denoising
• Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
• Structure-Texture Image Decomposition Using Deep Variational Priors
• Unified Single-Image and Video Super-Resolution via Denoising Algorithms

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