基于灰度变换及改进Retinex的低照度图像增强

Low-light Image Enhancement Based on Gray Scale Transformation and Improved Retinex

  • 摘要: 针对低光照条件下拍摄的图像受光和环境的影响,其重要信息丢失严重,出现对比度低、细节模糊等问题,提出了一种基于灰度变换与改进Retinex的图像增强方法。首先采用引力搜索算法(gravitational search algorithm, GSA)优化的全局灰度变换函数对图像的RGB各通道灰度图像进行灰度变换,增强图像光照强度,使其更接近均匀光照场景;然后将图像转为HSV色彩空间,对V通道(亮度通道)采用改进的多尺度Retinex(MSR)算法处理,将基于范围的自适应双边滤波和Gabor滤波作为Retinex算法的环绕函数,结合两种滤波的特性来增强图像的亮度和细节。最后采用伽马校正避免图像融合造成的图像色偏。实验结果显示,该方法处理过的增强图像在主观和客观评价上优于其他方法,图像颜色失真较小,细节更清晰,为图像的后续应用做了铺垫。

     

    Abstract: Aiming at the problems of low contrast and blurred details of images captured under low-light conditions due to the influence of light and the environment, important information is lost, and an image enhancement method based on grayscale transformation and improved Retinex is proposed. First, the global grayscale transformation function optimized by the gravity search algorithm(GSA) is used to perform grayscale transformation on the grayscale image of each RGB channel of the image to enhance the image illumination intensity and make it closer to the uniform illumination scene. The image is then converted to the HSV color space. The V channel (luminance channel) is processed by the improved multi-scale Retinex (MSR) algorithm; range-based adaptive bilateral filtering and Gabor filtering are used as the surround function of the Retinex algorithm, and the characteristics of the two filters are combined to enhance the brightness and detail of the image. Finally, gamma correction is used to avoid image color casts caused by image fusion. The experimental results show that the enhanced image processed by this method is better than that processed by other methods in subjective and objective evaluation, the color distortion of the image is smaller, and the details are clearer, which paves the way for the subsequent application of the image.

     

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