Image super-resolution reconstruction algorithm based on Bayesian theory

Zheng, Wenbo; Deng, Fei; Mo, Shaocong; Jin, Xin; Qu, Yili; Zhou, Jiangwei; Zou, R.; Su, Jia; Xie, Zefeng; Long, Sijie; Zheng, Chengfeng

doi:10.1109/iciea.2018.8398025

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…These approaches primarily rely on statistical methods to determine pixel values by constructing statistical relationship equations between pixels. Examples of such approaches include maximum likelihood estimation [8] and Bayesian estimation [9], which provide a rational statistical framework for SR. In comparison to interpolation-based methods, reconstruction-based methods generally yield sharper images.…”

Section: Introductionmentioning

confidence: 99%

DMSC-GAN: A c-GAN-Based Framework for Super-Resolution Reconstruction of SAR Images

Kong,

Liu

2023

Remote Sensing

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Synthetic Aperture Radar (SAR) imagery is significant in remote sensing, but the limited spatial resolution results in restricted detail and clarity. Current super-resolution methods confront challenges such as complex network structure, insufficient sensing capability, and difficulty extracting features with local and global dependencies. To address these challenges, DMSC-GAN, a SAR image super-resolution technique based on the c-GAN framework, is introduced in this study. The design objective of DMSC-GAN is to enhance the flexibility and controllability of the model by utilizing conditional inputs to modulate the generated image features. The method uses an encoder–decoder structure to construct a generator and introduces a feature extraction module that combines convolutional operations with Deformable Multi-Head Self-Attention (DMSA). This module can efficiently capture the features of objects of various shapes and extract important background information needed to recover complex image textures. In addition, a multi-scale feature extraction pyramid layer helps to capture image details at different scales. DMSC-GAN combines perceptual loss and feature matching loss and, with the enhanced dual-scale discriminator, successfully extracts features from SAR images for high-quality super-resolution reconstruction. Extensive experiments confirm the excellent performance of DMSC-GAN, which significantly improves the spatial resolution and visual quality of SAR images. This framework demonstrates strong capabilities and potential in advancing super-resolution techniques for SAR images.

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Section: Introductionmentioning

confidence: 99%

DMSC-GAN: A c-GAN-Based Framework for Super-Resolution Reconstruction of SAR Images

Kong,

Liu

2023

Remote Sensing

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“…To fuse high-resolution HSIs with low-resolution HSIs, Akhtar et al [26] proposed a general Bayesian sparse coding strategy that merges combines Bayesian dictionaries for reconstructing the image. Based on the Bayesian framework, Zheng et al [27] used a degenerate distribution method to derive an estimate of the analytical solution from enhancing the robustness of the algorithm. Irmak et al [28] converted the ill-posed SR reconstruction problem in the spectral domain of HSIs into a secondary optimization problem in the abundance map domain based on the energy minimization method of the Markov random field.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Feature Mapping Network for Hyperspectral Image Super-Resolution

et al. 2021

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Hyperspectral Image (HSI) can continuously cover tens or even hundreds of spectral segments for each spatial pixel. Limited by the cost and commercialization requirements of remote sensing satellites, HSIs often lose a lot of information due to insufficient image spatial resolution. For the high-dimensional nature of HSIs and the correlation between the spectra, the existing Super-Resolution (SR) methods for HSIs have the problems of excessive parameter amount and insufficient information complementarity between the spectra. This paper proposes a Multi-Scale Feature Mapping Network (MSFMNet) based on the cascaded residual learning to adaptively learn the prior information of HSIs. MSFMNet simplifies each part of the network into a few simple yet effective network modules. To learn the spatial-spectral characteristics among different spectral segments, a multi-scale feature generation and fusion Multi-Scale Feature Mapping Block (MSFMB) based on wavelet transform and spatial attention mechanism is designed in MSFMNet to learn the spectral features between different spectral segments. To effectively improve the multiplexing rate of multi-level spectral features, a Multi-Level Feature Fusion Block (MLFFB) is designed to fuse the multi-level spectral features. In the image reconstruction stage, an optimized sub-pixel convolution module is used for the up-sampling of different spectral segments. Through a large number of verifications on the three general hyperspectral datasets, the superiority of this method compared with the existing hyperspectral SR methods is proved. In subjective and objective experiments, its experimental performance is better than its competitors.

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