Defective Pixel Corrector for Line Scan and Area Scan Image Sensors

The defective pixel corrector is an essential component of the image processor, which detects and corrects defective pixels in the image. Processing discrete defective pixels that exist in the output of area-scan image sensors is the focus of current research. However, the case of defective columns produced by line scan image sensors is a massive challenge for existing algorithms. In this paper, we develop novel algorithms for the detection and correction of columnar defective pixels by modeling the properties of defective columns in the output images of line scan image sensors. In addition, we design the non-extremum verdict and texture adaptive correction for clustered defective pixels in the area scan sensors and apply them to current algorithms to get enhancement, and new algorithms are obtained. Moreover, we propose a generalized hardware architecture for detection and correction. In the experimental stage, the proposed methods are compared with the state-of-the-art methods, both at the algorithmic level and in hardware implementation. The experimental results show that, compared to the current widely used algorithms, our methods for line-scan image sensors improve the detection rate by 28%, the detection precision by 90%, and the image restoration quality by 35% with a 10% increase in hardware consumption. For area-scan image sensors, the proposed non-extremum verdict improves the detection rate by 25%, improves the precision from 0.056 to 0.983, and the texture adaptive correction mitigates the image blurring caused by the correction process, with a 37% improvement in image quality, while incurring only a 1% increase in hardware overhead. In addition, our proposed algorithms achieve comparable detection and correction results with far fewer computational and storage resource requirements than machine learning-based algorithms.