Image enhancement remains an intricate problem, crucial for image analysis. Several algorithms exist for the same. A few among these algorithms categorize images into different classes based on their statistical parameters and apply separate enhancement functions for each class. One such algorithm is the well-known adaptive gamma correction (AGC) algorithm. It works well for each class of images, but fails when the statistical parameters lie on the boundary of separation of two classes. We have developed an enhancement algorithm which can enhance images which lie on the boundary of separation equally well, as images which lie deep inside the boundary. The basic idea behind the algorithm is to combine the different enhancement functions of AGC using non-linear weight adjustments. Both contrast and brightness have been modified using these weight adjustments. We have conducted experiments on a data-set consisting of 9979 images. Results show that by using the proposed algorithm, average entropy of the enhanced images increases by 3.97% and average root mean square (rms) increases by 14.29% over AGC. Visual improvement is also perceivable.

图像增强仍然是一个复杂的问题，对于图像分析至关重要。存在几种相同的算法。这些算法中有一些基于图像的统计参数将图像分类为不同的类别，并对每个类别应用单独的增强功能。一种这样的算法是众所周知的自适应伽马校正（AGC）算法。它适用于每类图像，但当统计参数位于两类分离的边界时会失败。我们已经开发了一种增强算法，可以与位于边界深处的图像一样好地增强位于分离边界上的图像。该算法的基本思想是使用非线性权重调整来组合AGC的不同增强功能。对比度和亮度都已使用这些权重调整进行了修改。我们已经对包含9979张图像的数据集进行了实验。结果表明，通过所提出的算法，增强图像的平均熵增加了3.97％和平均均方根（rms）比AGC增加14.29 ％。视觉上的改善也是可以想到的。