Deep learning-based oracle image enhancement and calibration methods

As the earliest form of writing and historical cultural heritage in China, oracle bone inscriptions carry immeasurable density of historical information and potential for academic research. However, due to the age and preservation environment, the quality of many oracle bone images is severely impaired, resulting in problems such as text edge wear, loss of details, and blurred handwriting, which greatly limits the ability of scholars to accurately interpret the contents of the oracle bones and explore them in depth. In this paper, we propose a generative adversarial network (GAN)-based image enhancement and calibration system, which realizes multi-level information recovery and geometric correction from micro to macro by applying an advanced deep learning model to capture the minute features, delicate textures and original layouts of oracle bone images. The model in this paper consists of two main parts, that is, generator network G and discriminator network D. The task of generator G is to generate images consistent with the real data distribution based on random noise variables and possibly additional condition information (e.g., category labels, attributes, etc.), which should mimic or satisfy as much as possible the features of the real image under the given conditions. The discriminator D, on the other hand, is responsible for receiving the input real image or the fake image generated by the generator and outputs a probability value that reflects the probability that it considers the image to be a real sample, that is, the truthfulness or trustworthiness of the image. The model in this paper achieves the functions of image quality assessment and authenticity judgment, image recovery and optimization, image enhancement and proofreading by means of adversarial training. Experimental evaluations are carried out on two representative oracle image datasets, OBI-100 and OBI-300, and the effectiveness and superiority of this paper’s method in improving the clarity and readability of oracle images, as well as accurately recognizing oracle characters and extracting oracle information, are verified by comparing it with other image enhancement and reweighting methods. The effectiveness and superiority of the method is verified. The method of this paper provides a new technical means for the research and inheritance of oracle bone inscriptions.