Denoising x-ray images with deep learning: impact of spatially correlated noise

Deep learning algorithms have been applied to train denoising models for radiographs. Since obtaining full-dose reference images and low-dose training images through repeated scanning is not preferable or accessible, researchers simulate low-dose images through noise insertion. Traditionally, only white/uncorrelated noise is inserted into training images. However, the noise of flat-panel energy-integrated detectors is spatially correlated. In this work, we investigated the impact of spatial correlation on deep learning-based denoising. We applied spatial noise kernels of different strengths to the noise during noise insertion. Then, we compared the performance of denoising models that are trained with either uncorrelated-noise images or correlated-noise images. When the noise characteristic (uncorrelated or correlated) of test images matches that of the training images, the model can output images that are closest to the high-quality reference image. This implies that spatial correlation should be taken into consideration during image simulation and denoising model training.