Enhanced Data Augmentation for Denoising and Super-Resolution Reconstruction of Radiation Images

X-ray is widely used in security inspection systems for nondestructive testing, aiding inspection staff in identifying dangerous goods. Higher signal-to-noise ratio and resolution in radiation images can significantly enhance the staff’s ability to detect potential threats. This article introduces a novel data augmentation (DA) method aimed at improving the performance of neural network models for noise reduction and super-resolution (SR) reconstruction (such as EDSR, RCAN, and so on). The proposed method enables the models to be region-aware, allowing adaptive processing of different regions with varying degrees of noise or blurriness. Compared with traditional DA methods, the proposed method can effectively prevent the output image from being too smooth or producing artifacts while improving the performance of the models. Experiments indicate that the performance of the models trained with the proposed method shows consistent improvements, with the highest to be 0.26 dB in peak signal-to-noise ratio (PSNR).