Sharpening Image Details Using Local Phase Congruency Analysis

Shcherbinin, Andrey; Kolchin, Konstantin; Glazistov, Ivan; Rychagov, Mikhail

doi:10.2352/issn.2470-1173.2018.13.ipas-218

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Li et al [34] replaced the traditional Fourier transform with a fractional Fourier transform in their algorithm, preserving more image details. Shcherbinin et al [35] used local phase congruency analysis to sharpen image details. Jiang et al [36] proposed a novel rotation method to achieve higher-resolution digital imaging by utilizing higher-frequency diffraction patterns in the diagonal direction for resolution enhancement.…”

Section: Traditional Image Enhancement Methodsmentioning

confidence: 99%

A Dual Convolutional Neural Network with Attention Mechanism for Thermal Infrared Image Enhancement

Gao,

Zhang,

Wang

et al. 2023

Electronics

View full text Add to dashboard Cite

In industrial applications, thermal infrared images, which are commonly used, often suffer from issues such as low contrast and blurred details. Traditional image enhancement algorithms are limited in their effectiveness in improving the visual quality of thermal infrared images due to the specific nature of the application. Therefore, we propose a dual Convolutional Neural Network (CNN) combined with an attention mechanism to address the challenges of enhancing low-quality thermal infrared images and improving their visual quality. Firstly, we employ two parallel sub-networks to extract both global and local features. In one sub-network, we utilize a sparse mechanism incorporating dilated convolutions, while the other sub-network employs Feature Attention (FA) blocks based on channel attention and pixel attention. This architecture significantly enhances the feature extraction capability. The use of attention mechanisms allows the network to filter out irrelevant background information, enabling more flexible feature extraction. Finally, through a simple yet effective fusion block, we thoroughly integrate the extracted features to achieve an optimal fusion strategy, ensuring the highest quality enhancement of the final image. Extensive experiments on benchmark datasets and real images demonstrate that our proposed method outperforms other state-of-the-art models in terms of objective evaluation metrics and subjective assessments. The generated images also exhibit superior visual quality.

show abstract

Section: Traditional Image Enhancement Methodsmentioning

confidence: 99%