Research on Image Information Restoration Algorithm of Printing Micro Dots Based on GAN

During printing and shooting, the degradation of printing micro dots significantly affects the decoding and reading of hidden anti-counterfeiting information. However, existing image restoration methods cannot effectively restore image information. Moreover, there are relatively few datasets related to halftone dot images, and most datasets differ from the real data. Therefore, we propose an end-to-end restoration model based on the single-image super-resolution information. Specifically, we constructed a PMD dataset for real printing of anti-counterfeiting scenes. Based on this dataset, we used the high-resolution image information as the target. The positional inclination of the degraded images is corrected using the blank and interline characteristics of the printing micro dots images. The restoration is completed with the help of feature extraction and upsample of ESRGAN. In addition, we propose evaluation measures suitable for error detection, correction, and decoding requirements for microscopic image information. The experimental results show that, within the noise tolerance range, the image information restored by our method has a maximum average bit error rate is 0.97% and a Euclidean distance is 0.00804 pixels, whereas traditional filtering measures cannot effectively restore image information. The experimental results verified the effectiveness and robustness of the proposed method.