LAUSR

This paper presents the construction process of defective pixel detection and concealment methods, for image sensor online diagnosis and self-healing. The proposed process is based on pixel neighborhood analysis using only simple arithmetic operations on the image files. This leads to an optimization of the processing speed of the produced image. A first step in the process is to identify and locate the defective pixels on the image. Three defective pixel detection algorithms are proposed. The first one uses the distance between the pixel under test and its neighboring pixels. The second method is based on the median value of the pixel block around each pixel. The third method uses an evaluation and analysis of the local dispersion parameters in the image. The concealment of detected defective pixels is the second step of the self-healing process of image sensor. It consists of substituting the defective value by the median value of the neighborhood pixel block. In the study and learning phase, distorted images obtained by injecting random disturbances into healthy reference images are used to evaluate the defective pixel detection and concealment methods. The set of $1 176$ distorted images is constructed using 196 reference images with six types of defects, based on typical failure mechanism of image sensors. The proposed methods are compared to different state-of-the-art defective pixel detection and correction methods, in both software and $FPGA$FPGA implementations. The experimental results undoubtedly demonstrate that the new methods proposed in this paper perform the best results compared to the state-of-the-art.