Noise Elimination for Image Subtraction in Printed Circuit Board Defect Detection Algorithm

Zuwairie Ibrahim,Zulfakar Aspar,Wan Khairunizam Wan Ahmad,Sophan Wahyudi Nawawi,Kamal Khalil,Ismail Ibrahim,Muhammad Arif Abdul Rahim

doi:10.24297/ijct.v10i2.7000

Abstract

Image subtraction operation has been frequently used for automated visual inspection of printed circuit board (PCB) defects. Even though the image subtraction operation able to detect all defects occurred on PCB, some unwanted noise could be detected as well. Hence, before the image subtraction operation can be applied to real images of PCB, image registration operation should be done to align a defective PCB image against a template PCB image. This study shows how the image registration operation is incorporated with a thresholding algorithm to eliminate unwanted noise. The results show that all defects occurred on real images of PCB can be correctly detected without interfere by any unwanted noise.

Highlights

Nowadays, there are numerous algorithms, techniques, and approaches available in the area of automated visual inspection of defects
printed circuit board (PCB) inspection based on referential approaches is applied because it can be employed in hardware, and subsequently high processing rates are possible to be obtained [1]
The objective of this study is to introduce a noise elimination method to eliminate the unwanted noise, which is the residue of image subtraction operation

Summary

INTRODUCTION

There are numerous algorithms, techniques, and approaches available in the area of automated visual inspection of defects Those algorithms can be divided into three main approaches: referential approaches, rule-based approaches, and hybrid approaches [1]. A software-based approach is proposed in this paper to align the template image and defective image [5] These two images are subjected to image subtraction operation to produce a positive image and a negative image. The image subtraction operation is primarily used to reveal the differences between two images In such a way, it removes all objects that do not change while preserves those that do change in pixel value, as executed by Wu et al [2]. The black objects are yielded from the difference between the template image and defective image

Result

AND DISCUSSION

Underetch

CONCLUSION