PRN: progressive reasoning network and its image completion applications.

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Abstract

Ancient murals embody profound historical, cultural, scientific, and artistic values, yet many are afflicted with challenges such as pigment shedding or missing parts. While deep learning-based completion techniques have yielded remarkable results in restoring natural images, their application to damaged murals has been unsatisfactory due to data shifts and limited modeling efficacy. This paper proposes a novel progressive reasoning network designed specifically for mural image completion, inspired by the mural painting process. The proposed network comprises three key modules: a luminance reasoning module, a sketch reasoning module, and a color fusion module. The first two modules are based on the double-codec framework, designed to infer missing areas’ luminance and sketch information. The final module then utilizes a paired-associate learning approach to reconstruct the color image. This network utilizes two parallel, complementary pathways to estimate the luminance and sketch maps of a damaged mural. Subsequently, these two maps are combined to synthesize a complete color image. Experimental results indicate that the proposed network excels in restoring clearer structures and more vivid colors, surpassing current state-of-the-art methods in both quantitative and qualitative assessments for repairing damaged images. Our code and results will be publicly accessible at https://github.com/albestobe/PRN .© 2024. The Author(s).