Grain depot monitoring image denoising method and device based on deep learning, and medium

Abstract

The invention provides a grain depot monitoring image denoising method based on deep learning. The grain depot monitoring image denoising method comprises the following steps: acquiring an initial monitoring image of a grain depot; decomposing the initial monitoring image by using wavelet transform to obtain four sub-images; using the trained generative adversarial network model to perform de-noising processing on the high-frequency sub-graphs to obtain de-noised high-frequency sub-graphs meeting preset conditions; the generative adversarial network model comprises a generator for generating a de-noised sub-graph according to an input sub-graph and a discriminator for judging whether the de-noised sub-graph is a pure picture meeting a preset condition; and carrying out image reconstruction to obtain a de-noised monitoring image, thereby sharpening a low-quality grain depot image obtained by a grain depot monitoring system.

Description

technical field [0001] The invention relates to the field of image processing, in particular, to a method, device and medium for denoising grain depot monitoring images based on deep learning. Background technique [0002] In order to adapt to the development of the times and better manage food security issues, the construction of "smart grain depots" is booming. Intelligent security, target recognition and tracking and other equipment have also been widely used in the security of grain depots. The intelligent security video monitoring system can monitor the working conditions of some important places in the grain depot in real time, such as the main import and export grain storage channels, warehouse areas, operating points, equipment warehouses, drug warehouses, etc. At the same time, it can also warn abnormal behaviors such as personnel gathering, border crossing, regional intrusion, and operator violations, which not only reduces on-site inspections, but also reduces th...

AI Technical Summary

Problems solved by technology

However, due to the hardware conditions of the various image acquisition equipment in the grain depot and the limitations of the harsh environment, noise is inevitably generated during the image acquisition process, which will adversely affect the accuracy of subsequent image data analysis and processing results. Influence

Images