In this paper, a new context-based image contrast enhancement process using energy curve equalization (ECE) with a clipping limit has been proposed. In a fundamental anomaly to the existing contrast enhancement practice using histogram equalization, the projected method uses the energy curve. The computation of the energy curve utilizes a modified Hopfield neural network architecture. This process embraces the image's spatial adjacency information to the energy curve. For each intensity level, the energy value is calculated and the overall energy curve appears to be smoother than the histogram. A clipping limit applies to evade the over enhancement and is chosen as the average of the mean and median value. The clipped energy curve is subdivided into three regions based on the standard deviation value. Each part of the subdivided energy curve is equalized individually, and the final enhanced image is produced by combining transfer functions computed by the equalization process. The projected scheme's qualitative and quantitative efficiency is assessed by comparing it with the conventional histogram equalization techniques with and without the clipping limit.

中文翻译：

---

使用具有剪切限制的能量均衡的基于上下文的图像对比度增强


在本文中，提出了一种使用具有剪切限制的能量曲线均衡（ECE）的新的基于上下文的图像对比度增强过程。与使用直方图均衡的现有对比度增强实践的根本不同之处在于，投影方法使用能量曲线。能量曲线的计算利用了改进的 Hopfield 神经网络架构。该过程包含图像与能量曲线的空间邻接信息。对于每个强度级别，都会计算能量值，并且整体能量曲线看起来比直方图更平滑。剪裁限制适用于避免过度增强，并被选择为平均值和中值的平均值。根据标准偏差值，剪裁的能量曲线被细分为三个区域。细分的能量曲线的每个部分都被单独均衡，并且通过组合均衡过程计算的传递函数来产生最终的增强图像。通过将预测方案与有或没有限幅限制的传统直方图均衡技术进行比较来评估其定性和定量效率。