基于CLAHE和引导滤波的图像增强算法

Image Enhancement Algorithm Based on CLAHE and Guided Filtering

摘要: 为了解决荧光图像信号微弱和边界模糊的问题，提出了一种基于改进CLAHE和引导滤波的图像增强算法。该算法首先通过伽马变换调整图像整体亮度，方便后续图像操作。随后，利用小波变换分离图像低频和高频分量。对低频分量应用改进的CLAHE算法，通过自适应裁剪阈值控制图像对比度的增强幅度。同时对高频分量进行引导滤波，以突出更多细节。本文将改进的算法应用于膀胱脱落细胞的荧光染色图像，并与其他增强算法进行对比。结果表明，所提出来的算法克服了CLAHE算法处理后图像亮度过暗的问题，避免了HE和AHE算法在增强过程中常见的过度增强及细节丢失问题。该算法不仅有效提升图像对比度，还有效改善边缘和纹理细节的表现，对低照度或细节模糊图像的增强处理具有一定参考价值。

Abstract: An image enhancement algorithm based on improved CLAHE and guided filtering is proposed to solve the problem of weak signal and fuzzy boundary of fluorescence image. The algorithm first adjusts the overall brightness of the image by gamma transform, which is convenient for the subsequent image operation. Then, the low frequency and high frequency components of the image are separated by wavelet transform. The improved CLAHE algorithm is applied to the low frequency component, and the enhancement amplitude of image contrast is effectively controlled by adaptive clipping threshold. At the same time, the high frequency component is guided by filtering to highlight more details. In this paper, the improved algorithm is applied to the fluorescent staining images of bladder shedding cells and compared with other enhancement algorithms. The results show that the proposed algorithm can overcome the problem of too dark image brightness after CLAHE algorithm processing, and avoid the common problems of excessive enhancement and detail loss during the enhancement process of HE and AHE algorithm. This algorithm not only improves the image contrast, but also improves the performance of edge and texture details effectively, and has certain reference value for the enhancement processing of low illumination or blurred details.