A NMF-Based Image Restoration Scheme With Applications to LED Integrated Substrate Defect Detection

To examine surface quality of light emitting diode (LEI)) substrates, human visual inspection is commonly used. However, manual inspection is not reliable because patterns of an integrated substrate are small and repeating. This paper describes a machine vision method for auto-detecting defects embedded on LED substrates. Image of arranged substrates is first grabbed. A global image restoration scheme based on non-negative matrix factorization (NMF) is used to reconstruct a non-negative matrix approximation (NMA) using a matrix of lower dimension and a weight matrix with non-negative elements. Repeating patterns of NMF output, i.e., NMA, can be considered similar to its input counterpart that defects have been excluded. Defects thus can be efficiently revealed by subtracting grabbed image from NMA. A procedure to determine proper basis space size of a specific image using pseudo singular value and a procedure to initialize a proper pair of factors using non-negative double singular value decomposition are also described. The proposed method is compared with methods of Lu and Tsai (2005), Lu and Tsai (2008), Perng and Chen (2010) and Chen and Perng (2011). Overall, results show that G-measure for proposed method under conditions of normal and various exceptions is higher than other available low-rank approximation methods.