No-Reference Quality Metrics for Image Decolorization

Ayunts, Hrach; Agaian, Sos

doi:10.1109/tce.2023.3325744

Cited by 1 publication

(1 citation statement)

References 41 publications

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“…This metric, capturing both local characteristics and structural features, offers a comprehensive evaluation of image quality. By calculating the BIE metric for each enhanced image with varying parameters, our approach parallels the methodology employed in prior studies [33,34], ensuring both efficiency and accuracy in the enhancement process. The full steps for this process are described in Algorithm 1.…”

Section: Optimized Thermal Image Enhancement Using Genetic Algorithms...mentioning

confidence: 99%

Novel Entropy for Enhanced Thermal Imaging and Uncertainty Quantification

Ayunts,

Grigoryan,

Agaian

2024

Entropy

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This paper addresses the critical need for precise thermal modeling in electronics, where temperature significantly impacts system reliability. We emphasize the necessity of accurate temperature measurement and uncertainty quantification in thermal imaging, a vital tool across multiple industries. Current mathematical models and uncertainty measures, such as Rényi and Shannon entropies, are inadequate for the detailed informational content required in thermal images. Our work introduces a novel entropy that effectively captures the informational content of thermal images by combining local and global data, surpassing existing metrics. Validated by rigorous experimentation, this method enhances thermal images’ reliability and information preservation. We also present two enhancement frameworks that integrate an optimized genetic algorithm and image fusion techniques, improving image quality by reducing artifacts and enhancing contrast. These advancements offer significant contributions to thermal imaging and uncertainty quantification, with broad applications in various sectors.

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Section: Optimized Thermal Image Enhancement Using Genetic Algorithms...mentioning

confidence: 99%