脉冲神经网络加时间矩阵的明亮图像增强

K. Domínguez, M. M. Lavalle, Andrea Magadán Salazar y Gerardo Reyes Salgado
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引用次数: 0

摘要

数字图像被广泛应用于不同的领域,这些领域会受到各种因素的影响,从而影响图像的质量,降低了图像的正确分析。明亮的图像就是一个例子,亮度不允许正确检测不同的特征,例如边缘,纹理和颜色,同样地,影响人类的分析。在这项工作中,实现了第三代神经网络来增强明亮图像,特别是使用交集皮质模型和时间矩阵来修改像素值,从而获得更好的图像质量。实验结果表明,该模型具有较好的亮度增强效果。
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Pulsed Neural Net plus Time Matrix for Bright Images Enhancement
The digital images are widely used in diverse areas, these can be affected by diverse factors that affect its quality, which degrades its correct analysis. Bright images are an example of this, where the luminosity doesn’t allow the correct detection of different features for instance edges, textures and color, similarly, affect the human analysis. In this work a Third Generation Neuronal Network is implemented to enhancement bright images, specifically using the Intersection Cortical Model and a Time Matrix to modify the pixel value and obtain a better-quality image. The experiments shown that the proposed model is competitive to enhance bright images.
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