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Variational Models for Color Image Correction Inspired by Visual Perception and Neuroscience
Journal of Mathematical Imaging and Vision ( IF 2 ) Pub Date : 2020-07-10 , DOI: 10.1007/s10851-020-00978-1
Thomas Batard , Johannes Hertrich , Gabriele Steidl

Reproducing the perception of a real-world scene on a display device is a very challenging task which requires the understanding of the camera processing pipeline, the display process, and the way the human visual system processes the light it captures. Mathematical models based on psychophysical and physiological laws on color vision, named Retinex, provide efficient tools to handle degradations produced during the camera processing pipeline like the reduction of the contrast. In particular, Batard and Bertalmío (in J Math Imaging Vis 60(6):849–881, 2018) described some psychophysical laws on brightness perception as covariant derivatives, included them into a variational model, and observed that the quality of the color image correction is correlated with the accuracy of the vision model it includes. Based on this observation, we postulate that this model can be improved by including more accurate data on vision with a special attention on visual neuroscience here. Then, inspired by the presence of neurons responding to different visual attributes in the area V1 of the visual cortex as orientation, color or movement, to name a few, and horizontal connections modeling the interactions between those neurons, we construct two variational models to process both local (edges, textures) and global (contrast) features. This is an improvement with respect to the model of Batard and Bertalmío as the latter cannot process local and global features independently and simultaneously. Finally, we conduct experiments on color images which corroborate the improvement provided by the new models.



中文翻译:

视觉感知和神经科学启发的彩色图像校正变体模型

在显示设备上再现真实场景的感知是一项非常具有挑战性的任务,需要了解相机处理流程,显示过程以及人类视觉系统处理其捕获的光的方式。基于色觉的心理和生理规律的数学模型称为Retinex,它提供了有效的工具来处理相机处理流程中产生的退化,例如对比度降低。特别是,Batard和Bertalmío(在J Math Imaging Vis 60(6):849–881,2018年)中描述了一些关于亮度感知的心理物理定律作为协变导数,并将它们包含在变分模型中,并观察到彩色图像的质量校正与其所包含的视觉模型的准确性相关。基于这一观察,我们假设可以通过在视觉上添加更准确的数据(尤其是在视觉神经科学方面)来改善此模型。然后,受存在于神经皮层区域V1中的不同视觉属性(例如方向,颜色或运动)做出响应的神经元的启发,仅举几个例子,以及模拟这些神经元之间相互作用的水平连接,我们构造了两个变分模型来处理局部(边缘,纹理)和全局(对比度)特征。与Batard和Bertalmío的模型相比,这是一个改进,因为后者无法独立和同时处理局部和全局特征。最后,我们在彩色图像上进行实验,以证实新模型所提供的改进。

更新日期:2020-07-10
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