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In order to improve the image denoising ability, the wavelet transform (WT) and independent component analysis (ICA) are both introduced into image denoising in this paper. Although these two algorithms have their own advantages in image denoising, they are unable to reduce noises completely, which makes it difficult to achieve ideal effect. Therefore, a new image denoising method is proposed based on the combination of WT with ICA (WT-ICA). For verifying the WT-ICA denoising method, we adopt four image denoising methods for comparison: median filtering (MF), wavelet soft thresholding (WST), ICA, and WT-ICA. From the experimental results, it is shown that WT-ICA can significantly reduce noises and get lower-noise image. Moreover, the average of WT-ICA denoising image’s peak signal to noise ratio (PSNR) is improved by 20.54% compared with noisy image and 11.68% compared with the classical WST denoising image, which demonstrates its advantage. From the performance of texture and edge detection, denoising image by WT-ICA is closer to the original image. Therefore, the new method has its unique advantage in image denoising, which lays a solid foundation for the realization of further image processing task. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]