Watermark Detection in CMOS Image Sensors Using Cosine-Convolutional Semantic Networks

This article proposes a deep learning approach for automatic segmentation of low-contrast watermark defects on Complementary Metal Oxide Semiconductor (CMOS) Image Sensor (CIS) surfaces. The task of surface defect detection has been tackled by machine vision or deep learning methods. Traditional machine vision methods require expert domain knowledge for feature extraction, and they are computationally intensive. Deep learning methods based on Convolutional Neural Network (CNN) are extremely fast, but can be affected by illumination variations and perform poorly for low-contrast images. In this study, a U-Net type of encoder-decoder architecture is proposed. The Convolutional operation is substituted with the proposed Cosine-Convolutional operation in the encoder layers of the U-Net model with the objective of minimizing the effects of uneven illumination and highlighting the defect region. To further reduce the computation complexity and improve defect segmentation, an attention-like mechanism is used in the semantic encoder-decoder network. Pixel-wise multiplication is applied in the skip connections between encoder and decoder layers of the U-Net to replace the classical concatenation, thus reducing the parameter count of the model. The proposed methods are computationally fast, with an evaluation time of less than 10-ms for an image of $384\times 256$ pixels on a single low-end GPU. Experimental results show that the proposed Cosine-Convolutional encoder-decoder architectures can effectively and accurately be applied for defect detection and segmentation of watermarks in low-contrast CIS images.