Low contrast image is one of the major challenges in photography. The low contrast image not only poses difficulty to the interpretation of the scene but also causes trouble in the onward processing of the image for computer vision tasks. Histogram equalization (HE) is a traditional and widely used approach for contrast enhancement and applies to almost all types of images. However, HE causes over-enhancement of the image which degrades its natural appearance. In this paper, a novel scheme for enhancing the image contrast while retaining its naturalness has been proposed. The proposed method uses the compensated histogram equalization technique on each channel individually followed by blending of the channels with a suitable adaptive brightness adjustment kernel. The three color channels are combined to form the intermediate image. The high-frequency noise introduced during the process is filtered out. Finally, adaptive power law transformation is applied to adjust the overall brightness and to retain its naturalness. This makes the method strong in terms of contrast enhancement along with details preservation. The proposed method is applicable to all the contrast degraded images as it automatically adjusts its parameters based on the degradation level. The simulation results, on the CSIQ dataset, show that the proposed method performs better, qualitatively, and quantitatively than the existing methods.

低对比度图像是摄影的主要挑战之一。低对比度图像不仅给场景的解释带来了困难，而且还给计算机视觉任务的图像的后续处理带来了麻烦。直方图均衡（HE）是一种传统且广泛使用的对比度增强方法，适用于几乎所有类型的图像。但是，HE会导致图像过度增强，从而降低其自然外观。在本文中，提出了一种在保持图像自然度的同时增强图像对比度的新方案。所提出的方法在每个通道上分别使用补偿直方图均衡技术，然后将通道与合适的自适应亮度调整内核进行混合。将三个颜色通道合并以形成中间图像。在此过程中引入的高频噪声被滤除。最后，采用自适应幂律变换来调整整体亮度并保持其自然度。这使得该方法在对比度增强和细节保留方面很强。所提出的方法适用于所有对比度退化的图像，因为它会根据退化水平自动调整其参数。在CSIQ数据集上的仿真结果表明，所提出的方法比现有方法具有更好的定性和定量性能。所提出的方法适用于所有对比度退化的图像，因为它会根据退化水平自动调整其参数。在CSIQ数据集上的仿真结果表明，所提出的方法比现有方法具有更好的定性和定量性能。所提出的方法适用于所有对比度退化的图像，因为它会根据退化水平自动调整其参数。在CSIQ数据集上的仿真结果表明，所提出的方法比现有方法具有更好的定性和定量性能。