Low Contrast Surface Inspection under Uneven Illumination Using Independent Subspace Analysis

When using the machine vision technique to detect the low contrast surface image, the difference between the defect and the background of the low contrast surface is not obvious. The existing method is difficult to detect these defects, and the influence of uneven illumination makes the problem more difficult, the purpose of this paper is to detect defects in low-contrast surface images such as LCDs that are unevenly illuminated. The proposed method in this paper is based on the independent subspace analysis (ISA), which includes the learning and detection stage. In the training stage, a batch of defective low contrast images is used to find a set of independent basis images. During the test stage, the low contrast image to be measured is reconstructed by the nonlinear combination of a set of basis images obtained by learning, and the error between the reconstructed image and the sample to be measured is used to determine whether exist a defect. The experimental results show that the reconstruction image method based on the basis image through ISA has achieved good results. Introduction The quality of the product surface will directly affect the appearance and even the performance of the product. The surface defect detection technology based on machine vision plays an important role in modern manufacturing. as shown in Figure 1, LCD surface under the impact of uneven Illumination is difficult to identify the defects [1-3 ]. To solve the problem of uneven Illumination, The existing methods can be divided into three categories: 1) Illumination normalization, the use of image processing technology to pre-process the image and normalize the light, such as histogram equalization, Gamma correction, logarithmic transformation [4] and homomorphic filtering [5] and so on. 2) Extract the invariant feature of light, trying to extract the feature of the light changes robustly. Such as gradient graph, Retinex [6], quotient image [7], Local Binary Pattern [8], transform domain feature [9] and so on. 3) Model-based approach, it can be based on physical models or subspace models. In the physical modeling, the process of image formation model is based on the assumption that some objects reflect the surface properties, such as Lambertian reflectivity [10]; for subspace modeling mainly using statistical analysis techniques such as PCA (principal component analysis) [11] and ICA (independent component analysis)[12] [13]. The subspace transformation method avoids complicated feature design and measures the similarity of the testing sample through a simple rule in the transformation space. The simple matrix calculation makes the detection process simple and rapid and is immune to the influence of uneven illumination. Tsneg YH et al. [13] used base image representation to detect the low-contrast LCD surface's Mura defect on the influence of uneven illumination (Mura refers to uneven brightness of the display). It’s difficult to identify these Mura defects when they are under uneven illumination. Experimental results show that the proposed method can effectively detect various Mura defects in a low contrast LCD image under uneven illumination.