Night Vision Image De-noising of Apple Harvesting Robots Based on the Wavelet Fuzzy Threshold

Abstract

In this paper, the de-noising problem of night vision images is studied for apple harvesting robots working at night. The wavelet threshold method is applied to the de-noising of night vision images. Due to the fact that the choice of wavelet threshold function restricts the effect of the wavelet threshold method, the fuzzy theory is introduced to construct the fuzzy threshold function. We then propose the de-noising algorithm based on the wavelet fuzzy threshold. This new method can reduce image noise interferences, which is conducive to further image segmentation and recognition. To demonstrate the performance of the proposed method, we conducted simulation experiments and compared the median filtering and the wavelet soft threshold de-noising methods. It is shown that this new method can achieve the highest relative PSNR. Compared with the original images, the median filtering de-noising method and the classical wavelet threshold de-noising method, the relative PSNR increases 24.86%, 13.95%, and 11.38% respectively. We carry out comparisons from various aspects, such as intuitive visual evaluation, objective data evaluation, edge evaluation and artificial light evaluation. The experimental results show that the proposed method has unique advantages for the denoising of night vision images, which lay the foundation for apple harvesting robots working at night.

Fruit recognition based on pulse coupled neural network and genetic Elman algorithm application in apple harvesting robot

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Technology for Agriculture to increase food Production and Quality: A review

“…In this GOTO algorithm, the slave was set to slow-down to allow the master pass the slave safely in case there was a potential collision due to path overlap in the field. (Noboru Noguchi, Jeff Will, John Reid, Qin Zhang et al, 2004) [29] He proposed at night the de-noising problem of night vision. The images is studied for apple harvesting robots.…”