Virtual restoration of ancient Chinese paintings using color contrast enhancement and lacuna texture synthesis

This work presents a novel algorithm using color contrast enhancement and lacuna texture synthesis is proposed for the virtual restoration of ancient Chinese paintings. Color contrast enhancement based on saturation and de-saturation is performed in the u'v'Y color space, to change the saturation value in the chromaticity diagram, and adaptive histogram equalization then is adopted to adjust the luminance component. Additionally, this work presents a new patching method using the Markov random field (MRF) model of texture synthesis. Eliminating undesirable aged painting patterns, such as stains, crevices, and artifacts, and then filling the lacuna regions with the appropriate textures is simple and efficient. The synthesization procedure integrates three key approaches, weighted mask, annular scan and auxiliary, with neighborhood searching. These approaches can maintain a complete shape and prevent edge disconnection in the final results. Moreover, the boundary between original and synthesized paintings is seamless, and we are unable to distinguish in which the undesirable pattern appears.

I. Introduction

The uses of digital processing for image improvement recently has received considerable interest and has broad applications in medical imagery, remote sensing, digital multimedia, image transmission, and so on. The digital processing includes four types of image manipulation processes, namely: image compression, image enhancement, image restoration and geometrical image modification [1]. The main objective of image enhancement techniques is to process an image, and producing a new image that is more suitable than the original for certain specific applications [2]. No general unifying theory and image quality standard exists that can suggest design criteria for image enhancement processing. Moreover, processing image enhancement for restoration and reconstruction becomes even more difficult when dealing with ancient paintings.