Mine Remote Monitoring Image Denoising Algorithm Based on Generated Residuals Learning
In the remote monitoring system of mine,due to the restrictions of the location and environment of the monito-ring camera,there are often various noises in the image,such as salt and pepper noise,Gaussian noise,etc.,which will have a serious impact on the image quality,but also bring great difficulties to the subsequent image analysis and processing.Therefore,how to accurately remove noise and improve image quality is always an important problem in mine remote monitoring system.Generative adversarial learning is an image processing technique based on Generative Adversarial Networks(GAN),which can effectively remove noise from images.In this paper,a mine remote monitoring image denoising algorithm based on generation re-siduals learning is proposed.The algorithm first learns a group of residual images by GAN generator,and then adds the original image and residual image by the way of residual learning to get the denoised image.At the same time,in order to improve the robustness and applicability of the algorithm,noise distribution estimation network and adaptive control mechanism are intro-duced.The experimental results show that the algorithm can effectively remove the noise in the remote monitoring image of mine,and has good robustness and applicability.
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