Detecting defects in repeatedly patterned image with spatially different level of noise

Defect detection is to find unexpected peak regions in an inspection image. Stable Principal Component Pursuit (SPCP) decomposes a given image into three matrices, low-rank, sparsity, and noise which are used for detecting defects. Each of them contains repeated pattern, spatially narrow abnormal elements which are regarded as defects, and small magnitude elements respectively. However, if the noise level of the image is spatially varied, it is hard to separate noise appropriately using naive SPCP. To overcome the difficulty, we propose a novel sliding-window based SPCP algorithm. First, a repeated pattern of each sliding-window is converted to a matrix for SPCP. The noise level based on rank-one approximation is then estimated, and the matrix decomposition is performed. Finally, the sparsity values of all sliding-windows are merged by averaging, and then the averaged term is used for defect detection. The experimental results show that our algorithm outperforms the traditional approaches.