Improved Binarization Using Morphologydriven Image Resizing and Decomposition This paper presents a novel binarization algorithm for stained decipherable patterns. First, the input image is downsized, of which the reduction ratio is determined by iteratively applying binary morphological Closing operation. Such morphologydriven image downsizing not only saves the computation time of subsequent processes, but the key features necessary for the successful decoding is preserved. Then, high or low contrast areas are decomposed by applying the grayscale morphological Closing and Opening operators to the downsized image, and subtracting the two resulting output images from each other. If necessary, these areas are further subjected to decomposition to obtain finer separation of high and low regions. Having done the preprocessing, two approaches are proposed to do the binarization: (1) GMM is used to estimate a binarization threshold for each region (2) the binarization problem is treated as an imagetranslation task and hence a deep learning approach based on the conditional generative adversarial network (cGAN) is trained using the high or low contrast areas as conditional inputs. Our method solves the difficulty of choosing a proper preset sampling mask in conventional adaptive thresholding methods. Extensive experimental results show that the binarization algorithm can efficiently improve the decipher success rate over the other methods.

Generating Realistic Binarization Data with Generative Adversarial Networks One of the limitations for using Deep Learning models to solve binarization tasks is that there is a lack of large quantities of labeled data available to train such models. Efforts to create synthetic data for binarization mostly rely on heuristic image processing techniques and generally lack realism. In this work, we propose a method to produce realistic synthetic data using an adversarially trained image translation model. We extend the popular CycleGAN model to be conditioned on the ground truth binarization mask as it translates images from the domain of synthetic images to the domain of real images. For evaluation, we train deep networks on synthetic datasets produced in different ways and measure their performance on the DIBCO datasets. Compared to not pretraining, we reduce error by 13% on average, and compared to pretraining on unrealistic data, we reduce error by 6%. Visually, we show that DGTCycleGAN model produces more realistic synthetic data than other models.