Digital inverse halftoning technology refers to the process of converting halftone images back into continuous-tone images that are similar to the original image. Inverse halftoning is an ill-posed problem, and many traditional algorithms and current deep-learning methods attempt to solve this problem. However, the restored image still contains some unexpected artifacts and details. For deep learning methods, accurately learning the styles of different halftone patterns is extremely challenging. For example, a model trained on a specific uniform distribution of halftone images may not be applicable to other halftone forms.
Many image processing techniques are designed for grayscale images, such as enlargement, reduction, brightness and contrast adjustment, resolution adjustment, or other geometric transformations. If halftone images are processed directly, it may severely degrade the halftone quality. Therefore, there is a need for digital halftoning development.
