This paper presents a novel image contrast enhancement technique that uses exposure-based energy curve equalization (ECE) with a plateau limit. In a primary deviation from the current histogram equalization process for contrast enhancement, the proposed approach uses an energy curve for the same. The energy curve is computed based on the modified Hopfield neural network architecture, which contains spatial context information. The calculated energy curve is clipped with a plateau limit computed as the average of the energy curve. The exposure threshold is computed and used to divide the clipped energy curve. The two resulting energy curves are equalized independently, and the final enhanced image is generated by integrating the images achieved by transforming the equalized energy curves. The performance of the proposed method is evaluated on a variety of low contrast images. The subjective and objective evaluations of the proposed method are compared with the various histogram equalization (HE) based methods and other state-of-the-art methods to exemplify the effectiveness.

中文翻译：

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用于增强对比度失真图像的基于曝光的能量曲线均衡

本文提出了一种新颖的图像对比度增强技术，该技术使用具有平台限制的基于曝光的能量曲线均衡 (ECE)。在与当前用于对比度增强的直方图均衡过程的主要偏差中，所提出的方法使用相同的能量曲线。能量曲线是基于改进的 Hopfield 神经网络架构计算的，其中包含空间上下文信息。计算出的能量曲线被一个平台限制剪裁，该平台限制计算为能量曲线的平均值。计算暴露阈值并用于划分剪裁的能量曲线。两条得到的能量曲线独立均衡，最终的增强图像是通过对均衡能量曲线变换得到的图像进行积分而产生的。在各种低对比度图像上评估了所提出方法的性能。将所提出方法的主观和客观评估与各种基于直方图均衡化 (HE) 的方法和其他最先进的方法进行比较，以证明其有效性。