苹果采摘机器人夜间图像降噪算法

苹果采摘机器人图像处理系统采集到的实时夜间图像含有大量的噪声,影响采摘效率。通过差影法对夜间图像进行噪声分析,判定其噪声类型为以高斯噪声为主,并伴有部分椒盐噪声的混合噪声。针对高斯噪声去除难题,将独立成分分析(independent component analysis,ICA)理论引入夜间图像降噪,并尝试采用粒子群优化算法(particle swarm optimization,PSO)对ICA进行优化,建立基于PSO优化的ICA降噪算法(PSO-ICA),以期最大限度地降低夜间图像的噪声污染。利用标准Lenna图像和自然光下的苹果图像,进行仿真试验,结果表明PSO-ICA方法降噪效果最为理想。然后对白炽灯、荧光灯、LED灯3种不同的人工光源下采集到10个样本点的夜间图像进行验证试验,结果表明,从视觉效果评价,在3种人工光源环境下,PSO-ICA降噪方法得到低噪图像均表现为噪点明显减少;从相对峰值信噪比(relative peak signal-to-noise ratio,RPSNR)看,在3种人工光源下的平均值,PSO-ICA得到的低噪图像,分别比原始图像、均值滤波降噪和ICA降噪得到的图像的相对峰值信噪比提高21.28%、12.41%、5.53%;从运行时间看,PSO-ICA方法较ICA方法的运行时间平均减少了49.60%。PSO-ICA方法用于夜间图像降噪有着独到的优势,为实现苹果采摘机器人的夜间作业打下坚实的基础。

De-noising algorithm of night vision image for apple harvesting robot

As apple harvesting needs large amount of labor, and the seasonality is strong, the night operation of apple harvesting robot is proposed, in order to improve the efficiency of harvesting. The apple's real-time night vision image contains lots of noise, which is captured by image processing system of apple harvesting robot. The noise will influence the operating efficiency and recognition precision, and then influence the harvesting efficiency. Under different artificial lights, apple night vision images are captured, the noises are analyzed through the difference image method, and the type of noise is determined to be mixed noise. The main part of mixed noise is Gaussian noise, accompanied by some salt-pepper noise. Aiming at the problem of Gaussian noise removal, the theory of independent component analysis (ICA) is introduced into the de-noising method for night vision image. The ICA algorithm mostly uses gradient iterative solver, so it has some defects, such as easily trapped in local minimum, slow convergence speed. All of these defects lead to the following phenomena easily, such as the unthoroughness in the de-noising and the long running time. In order to overcome these defects, particle swarm optimization (PSO) algorithm is used to optimize the ICA, further to establish an optimized ICA de-noising method based on PSO (PSO-ICA), applied in night vision image, hoping to minimize noise pollution and improving the operating efficiency of de-noising method. Using the standard Lenna image and apple image captured under nature light, by the simulation experiments, these 2 pictures are added with the Gaussian noise with the variance of 0.05 and the salt-pepper noise with the P value of 0.05, respectively. Compared with the average filtering method and ICA de-noising method, the results show that the de-noising effect of PSO-ICA algorithm is the most ideal. Using peak signal-to-noise ratio (PSNR) to do difference test, the result shows that, under 0.05 significant level, 3 de-noising methods show significant difference. Using different apple night vision images captured to do experiments, the results show that, from the visual evaluation, the low noise image is obtained by PSO-ICA de-noising method, and its noise decreased significantly. In order to evaluate the de-noising effect of night vision image more objectively, taking the natural light image as reference, the concept of relative peak signal-to-noise ratio (RPSNR) is proposed. From the RPSNR evaluation, compared with the original image, the image after average filtering de-noising and that after ICA de-noising, the image based on the method of PSO-ICA de-noising increased on average by 21.28%, 12.41% and 5.53%, respectively. From the run time evaluation, PSO algorithm has greatly improved the efficiency of ICA algorithm. Under incandescent lamp, the night vision image and its de-noised images have the highest RPSNR, so this type of light is suitable for artificial light source. Finally, under the natural light and 3 different artificial lights, 10 images of natural light and 30 night images are captured from 10 sample points. Using all of these images to do the repeated experiments, the trends of experimental results are consistent. In conclusion, PSO-ICA algorithm has unique advantage for night vision image de-noising, which provides a solid foundation for the night operation of apple picking robot.