Self-Adaptive Anisotropic Image Enhancement Algorithm Based on Local Variance

Abstract

When an image is enhanced using the traditional Laplacian enhancement model, the enhancement effect is evident but the overshoot phenomenon occurs simultaneously as the neighborhood and weight increase. To solve the contradiction between edge preservation and noise suppression during the image enhancement process, this study proposed an improved partial differential equation image enhancement algorithm. The algorithm combined the neighborhood features of digital images, mined the relationship between the local variance and the detail information of images, and classified the image noise and detail information with local variance. On this basis, the periodic change features of the trigonometric function were used to construct anisotropic diffusion coefficients, the numerical solution method of the partial differential equation was used to calculate the numerical solution of the proposed algorithm, and simulation experiment was implemented to analyze the influence of the algorithm model parameters on enhancement effect. Lastly, the anisotropic enhancement algorithm proposed in this study was validated through comparison with the traditional Laplace algorithm. Results indicate that tangential direction parameter and the number of iterations exert influence on the image enhancement effects. That is, when tangential direction parameter is set as 0.5 and the iterative operation is conducted 5 times, the algorithm smoothens the noise and enhances the image details. When the proposed algorithm is used to process low-contrast industrial image, the signal-to-noise ratio of the enhanced image is increased by 8 db and the mean squared error is reduced by 55% compared with the enhancement effect of the traditional Laplace enhancement method. In terms of subjective visual effect, the noise of the enhanced image is filtered, whereas detailed information (e.g., numerous weak edges) is reserved. Hence, subjective and objective evaluations reflected that the proposed algorithm can effectively enhance image details and avoid over-enhancement. Moreover, the proposed algorithm is of favorable adaptability and certain reference value to low-contrast image enhancement.

A Review on Performance Analysis of PDE based Anisotropic Diffusion Approaches for Image Enhancement

“…These separate diffusion coefficients for smoothing and sharpening improve the control of the diffusion process in radiography. Chen et al [59] gives selfadaptive tangential and normal diffusion coefficients. Diffusion coefficient α based on local variance 𝜎 2 and threshold T is given as:…”

Investigation of Key Parameters to Improve Denoising Images based on Diffusion Equation

“…For many years, many articles analyzed the capability and drawbacks of the PM method to propose modified methods [8], [9], [10], [11], [12], [13], [14]. In recent years some hybrid models based on the PM method are proposed that show remarkable results [15], [16], [17], [18], [19], [20], [21]. For a gray-scale image in continous domain, Perona and Malik proposed an edge-controlled diffusion operator [7]:…”