为了提高采摘机器人的适用性和工作效率,保证成熟苹果果实的及时采摘,需要机器人具有夜间连续识别、采摘作业的能力。针对夜间苹果图像的特点,该文提出一种基于引导滤波的具有边缘保持特性的Retinex图像增强算法。利用颜色特征分量采用具有边缘保持功能的引导滤波来估计出照度分量；进而利用单尺度Retinex算法对图像进行对数变换获得仅包含物体本身特性的反射分量图像；分别对照度分量和反射分量图像增强后,再合成为新的夜间苹果的增强图像。文中选取30幅荧光灯辅助照明下采集到的夜间苹果图像进行试验的结果显示,该文增强算法处理后的30幅图像的平均灰度值,分别比原始图像、直方图均衡算法、同态滤波算法和双边滤波Retinex算法处理后的图像平均提高230.34%、251.16%、14.56%、7.75%,标准差平均提高36.90%、-23.95%、53.37%、28.00%,信息熵平均提高65.88%、99.68%、66.85%、17.53%,平均梯度提高161.70%、64.71%、139.89%、17.70%。且该文算法较双边滤波Retinex方法的运行时间平均减少74.56%。表明该文算法在夜间图像增强效果和运行时间效率上有明显的提高,为后续夜间图像的分割和目标识别提供了保障。

In order to improve the adaptability and working efficiency of apple harvesting robot used to promptly pick the ripe apples, the harvesting robot has to work continuously at night.But the night vision image of apple has many dark spaces and shadows besides the low resolution.These negative factors bring difficulties for the harvesting robot to work at night.So this paper proposes an edge preserving Retinex algorithm based on guided filtering to enhance apple night vision image.The illumination component is estimated by using the guided filtering which can be used as an edge preserving smoothing operator, and then it is removed from the original image to obtain the reflection component with its own characteristics.After Gamma correction, the 2 parts of the image are synthesized into a new image.Finally the night vision image of apple is enhanced.The specific implementation process is stated as follows: firstly, an apple night vision image of the RGB (red, green, blue) is converted into HSI(hue, saturation, intensity) color space.Then the intensity of the image is processed by the guided filtering which has a function of edge preserving.This algorithm is able to accurately estimate the illumination of the image in the edge area with high contrast.After that, a single scale Retinex algorithm is used for logarithmic transform to get the reflection image.Then, the Gamma corrections are used for reflection component and illumination component.The 2 parts of the image are synthesized into a new image and the resulting image is converted into RGB color space.Finally, the output of the apple image is the target enhancement image.This paper selects 30 apple night vision images collected under fluorescent lighting to make simulation experiment compared with histogram equalization algorithm, homomorphic filtering algorithm and Retinex algorithm based on bilateral filtering.From the visual effects, the 4 methods have a certain degree of enhancement.By using the histogram equalization algorithm, not only the brightness has improved greatly, but also the reflective part of the apple is magnified.And the apple in the dark area is not fully displayed.After using the homomorphic filtering algorithm, the night vision image is enhanced and the enhancement effect of the highlight reflective area is poorer.After using the bilateral filtering algorithm, the edge is also maintained as this proposed algorithm, but there is a circle of white halo at the area of the apple gradient mutation.However, after using the proposed algorithm to enhance the images, the apple fruit is more prominent.Its details are clearly visible in the dark areas and there is no phenomenon of over enhancement.There are more obvious visual effect and clearer outline of the target fruit, and the halo part has been well suppressed.According to the objective performance indices of experiment results, it shows that the mean grey value of the 30 images after processed by the proposed method, compared with the original image, histogram equalization algorithms, homomorphic filtering algorithm and Retinex algorithm based on bilateral filtering, increases averagely by 230.34%, 251.16%, 14.56% and 7.75%, respectively, the standard deviation on average increases by 36.90%, -23.95%, 53.37% and 28.00%, respectively, the information entropy increases averagely by 65.88%, 99.68%, 66.85% and 17.53%, respectively, and the gradient on average increases by 161.70%, 64.71%, 139.89% and 17.70%, respectively.The enhancement effect of proposed algorithm is superior to other 3 algorithms.In addition, compared with the Retinex algorithm based on bilateral filtering, the proposed algorithm has an average reduction of 74.56% in processing time, which reflects the timeliness and efficiency.In conclusion, this algorithm has an unique advantage for night vision image enhancement.So it can satisfy the actual demands and realize the continuous operation of apple harvesting robot at night.

TP24,TP391

国家自然科学基金资助项目(31571571,61203014)；教育部高等学校博士点基金资助项目(20133227110024)；江苏省高校优势学科建设工程资助项目(PAPD)