Pixel-defect corrections for radiography detectors based on deep learning

Flat-panel radiography detectors employ the thin film transistor (TFT) panels to acquire high-quality x-ray images. Pixel defects in the TFT panel can degrade the image quality and lower the production yield of the panel, and ultimately increase the production cost. Hence, developing an appropriate defect correction algorithm for acquired images is important. Conventional algorithms are based on statistical learning and hence optimizing their performances is difficult especially for image edge parts. To alleviate this problem, a template matching technique can be used. In this paper, we considered various pixel-defect correction algorithms based on deep learning techniques, such as the artificial neural network (ANN), convolutional neural network (CNN), and generative adversarial networks, and compared their performances. The defect-correction performances are compared using practical x-ray images acquired from general radiography detectors. A concatenate CNN showed the best defect-correction performance. We also showed that a single-layer ANN could conduct an efficient defect correction in terms of both correction and computational complexity performances.