Abstract
In this paper, an image enhancement algorithm is presented for identification of corrosion areas and dealing with low contrast present in shadow areas of an image. This algorithm uses histogram equalization processing under the hue-saturation-intensity model. First of all, an etched image is transformed from red-green-blue color space to hue-saturation-intensity color space, and only the luminance component is enhanced. Then, part of the enhanced image is combined with the original tone component, followed by saturation and conversion to red-green-blue color space to obtain the enhanced corrosion image. Experimental results show that the proposed method significantly improves overall brightness, increases contrast details in shadow areas, and strengthens identification of corrosion areas in the image.
Highlights
In several applications, equipment made up of metallic material is widely used
Image enhancement algorithms mainly include single-scale Retinex (SSR) and multiscale Retinex (MSR) enhancement methods, which are based on the color sense consistency model [5,6,7], direct enhancement method aiming at pixel grayscale [8, 9], homomorphic filtering enhancement method [10, 11], enhancement method of defogging with respect to the dark channel priori theory [12, 13], image enhancement algorithm based on wavelet transform, and histogram equalization algorithm [14, 15]
Experimental Environment. is paper proposes an image enhancement algorithm for observation of metal corrosion areas. e algorithm is mainly applied for image enhancement after pipeline image acquisition, and it is useful for detection and identification of subsequent corrosion morphology. erefore, in order to verify the enhancement effect of the algorithm on the metal pipe image, images of a metal pipe corroded due to exposure to natural environment are selected. e photos of the metal pipes are given in Figure 5, where the corrosion can be observed
Summary
Equipment made up of metallic material is widely used. Under the influence of the external environment, the metallic material will be corroded, causing the appearance of several features on the surface of the corrosion area, such as morphology, texture, grayscale, and color. e degree and type of corrosion in the material can be identified by analyzing these features. Under the influence of the external environment, the metallic material will be corroded, causing the appearance of several features on the surface of the corrosion area, such as morphology, texture, grayscale, and color. As shown in the example given in the previous subsection, histogram equalization enhances the whole image. A corrosion image with different scales and corrosion details cannot be effectively enhanced using only the equalization. Is paper uses this transform to make differential improvements to the image enhanced using the histogram equalization and modify the image background with different contrast scales. The two-dimensional discrete wavelet transform is used to decompose the enhanced image obtained after histogram equalization, referred to as enhanced Figure 1. Nonlinear filtering operation is applied to the decomposed image group to obtain a filtered image group. The image group is recombined based on Kj, resulting in an enhanced image referred to as enhanced Figure 2
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