Water Gauge Image Denoising Model Based on Improved Adaptive Total Variation

As an important part of water level warning in water conservancy projects, often due to the influence of en-vironmental factors such as light and stains, the acquired water gauge images have sticky, broken and bright spot conditions, which affect the identification of water gauges. To solve this problem, a water gauge image denoising model based on improved adaptive total variation is proposed. Firstly, the regular term exponent in the adaptive total variational equation is changed to an inverse cosine function; secondly, the differential curvature is used to distinguish the image noise points and increase the smoothing strength at the noise points; finally, according to the characteristics of the gradient mode and adaptive gradient threshold after Gaussian filtering, the New model can adaptively denoise in the smooth area and protect the edge area, so as to have the characteristics of both edge-preserving denoising. The experimental results show that the new model has a great improvement in image vision, higher iteration efficiency and an average increase of 1.6 dB in peak signal-to-noise ratio, and an average increase of 9% in structural similarity, which is more beneficial to practical applications.