Computer vision detection technology is one of the most popular topics in the field of computer vision. With the continuous improvement of the relevant algorithm and the performance-price ratio of the corresponding imaging equipment, the corresponding computer vision detection algorithm is also constantly upgraded and deepened. Computer vision detection technology is mainly used in transportation, public security, national defense and military fields, but the pixel accuracy of traditional computer vision detection technology has been unable to meet today's accuracy requirements. In this paper, firstly, the quantum denoising algorithm based on dual-tree and dual-density wavelet transform is used to realize the combination of quantum image coding expression and wavelet transform, and finally achieve a more detailed and accurate description of the image and realize the noise reduction of the image. In order to further realize sub-pixel image processing, cubic spline interpolation edge detection algorithm will be added to wavelet transform, which mainly calculates the zeros of the second-order function corresponding to the cubic spline function on both sides of the image edge points, so as to realize sub-pixel location of the image edge points. Finally, by comparing with the traditional pixel accuracy detection algorithms, it can be found that the proposed subpixel computer vision detection algorithm based on wavelet transform has good robustness, and its computing time is relatively faster, so it will have better adaptability in practical applications. INDEX TERMS Wavelet transform, vision detection algorithm, quantum denoising algorithm, subpixel vision detection algorithm.
Background Maize is one of the most important food crops worldwide. Roots play important role in maize productivity through water and nutrient uptake from the soil. Improving maize root traits for efficient water uptake will help to optimize irrigation and contribute to sustainable maize production. Therefore, we investigated the protein profiles of maize cv. Anyu308 root system divided into Upper root zone (UR), Middle root (MR), and Lower root (LR), by label free quantitative shotgun proteomic approach (LFQ). The aim of our study was to identify proteins and mechanisms associated with enhanced water uptake in different maize root zones under automatic irrigation system. Results At field capacity, MR had the highest water uptake than the UR and LR. We identified a total of 489 differentially abundant proteins (DAPs) by pairwise comparison of MR, LR, and UR. Cluster analysis of DAPs revealed MR and UR had similar protein abundance patterns different from LR. More proteins were differentially abundant in MR/UR compared to LR/MR and LR/UR. Comparisons of protein profiles indicate that the DAPs in MR increased in abundance, compared to UR and LR which had more downregulated DAPs. The abundance patterns, functional category, and pathway enrichment analyses highlight chromatin structure and dynamics, ribosomal structures, polysaccharide metabolism, energy metabolism and transport, induction of water channels, inorganic ion transport, intracellular trafficking, and vesicular transport, and posttranslational modification as primary biological processes related to enhanced root water uptake in maize. Specifically, the abundance of histones, ribosomal proteins, and aquaporins, including mitochondrion electron transport proteins and the TCA cycle, underpinned MR’s enhanced water uptake. Furthermore, proteins involved in folding and vascular transport supported the radial transport of solute across cell membranes in UR and MR. Parallel reaction monitoring analysis was used to confirmed profile of the DAPs obtained by LFQ-based proteomics. Conclusion The list of differentially abundant proteins identified in MR are interesting candidates for further elucidation of their role in enhanced water uptake in maize root. Overall, the current results provided an insight into the mechanisms of maize root water uptake.
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