Abstract
When capturing images under low-light lighting conditions, the resulting images often suffer low visibility. Such low-visibility images not only affect the visual effect but also cause many difficulties in practical application. Therefore, image enhancement technology under low-light conditions has always been a challenging problem in image algorithms. Considering that most of the existing image enhancement methods are based on the RGB color space enhancement technology, the correlation among the RGB three primary colors is ignored, which makes the color distortion phenomenon easy to occur when the image is enhanced. To solve the problems of poor image visibility and color deviation under low-light conditions, in this paper an advanced Retinex network enhancement method is proposed. In the method, firstly the low-light RGB image is transformed into HSV color space, the Retinex decomposition network is used to decompose and enhance the value component separately, and thus increasing the resolution of the value component through up-sampling operation; then, for the hue component and saturation component, the nearest neighbor interpolation is used to increase their resolutions, and the enhanced value component is combined to convert back to RGB color space to obtain the initial enhanced image; finally, the wavelet transform image fusion technology is used to fuse with the original low-light image to eliminate the over-enhanced part in the initial enhanced image. The analysis of experimental results shows that the method proposed in this paper has obvious advantages in brightness enhancement and color restoration of low-light images. Especially, comparing with the original Retinex network method, the NIQE value decreases by an average of 19.49%, and the image standard deviation increases by an average of 41.35%. The algorithm proposed in this paper is expected to be effectively used in the fields of security monitoring and biomedicine.
Highlights
image enhancement technology under low-light conditions has always been a challenging problem in image algorithms
most of the existing image enhancement methods are based on the RGB color space enhancement technology
firstly the low-light RGB image is transformed into HSV color space
Summary
基于人眼对视觉的感知, 在 HSV 色彩空间中 颜色由色相 (hue, H ), 饱和度 (saturation, S ), 明 度 (value, V ) 分量共同决定, 3 个分量相互独立, 可以对各色彩分量进行单独的提取与变换. 而在 RGB 色彩空间中红绿蓝 3 个分量相互耦合在一 起, Retinex-Net 方法直接对 RGB 图像进行增强 处理破坏了各颜色通道之间的相互关系, 致使色彩 失真. 在处理低照度图像时, 本文提出仅对明度分 量进行增强, 这有利于在色彩增强过程中保持原本. 基于人眼对视觉的感知, 在 HSV 色彩空间中 颜色由色相 (hue, H ), 饱和度 (saturation, S ), 明 度 (value, V ) 分量共同决定, 3 个分量相互独立, 可以对各色彩分量进行单独的提取与变换. 而在 RGB 色彩空间中红绿蓝 3 个分量相互耦合在一 起, Retinex-Net 方法直接对 RGB 图像进行增强 处理破坏了各颜色通道之间的相互关系, 致使色彩 失真. 如图 1(a) 所示, Retinex 理 论认为人类所观测的图像由照明分布情况和物体 对光的反射情况组合决定, 并且物体对光的反射能 力是物体本身的固有属性, 其不随光照条件的变化 而改变. 这里对 Retinex 的算术思想进行一个简单 的介绍, 在对 (2) 式进行对数变换后, 可以得到 lg [S(x, y)] = lg [I(x, y) · R(x, y)] = lg [I(x, y)] + lg [R(x, y)] . 图 2 为本文所提算法的结构框架, 其中分解网 络依据 Retinex 理论, 在 V 分量上进行低照度图 像到正常照度图像之间的映射关系学习, 把低照度 图像的 V 分量分解为反射图像和照明图像 2 个分 量. 图 1 (a) Retinex 成像理论模型; (b) Retinex 算术思想简介 Fig. 1. (a) Retinex imaging theoretical model; (b) arithmetic ideas of Retinex algorithm. 图 2 改进的 Retinex 网络增强算法流程图 Fig. 2. Flow chart of the advanced Retinex network enhancement algorithm
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
