Article: A colour restoration method for multi-texture oil painting images based on colour transfer Journal: International Journal of Business Intelligence and Data Mining (IJBIDM) 2026 Vol.28 No.9 pp.117 - 132 Abstract: Multi-texture oil painting images face challenges such as complex texture interference and artistic feature distortion in the process of colour restoration, making it difficult to accurately restore colour layers and maintain stroke details. Therefore, colour restoration method for multi-texture oil painting images based on colour transfer is proposed. Firstly, the low dimensional subspace denoising method combining RPCA and BM3D effectively removes image noise while preserving texture features. Secondly, design a colour transfer strategy that integrates brightness remapping and K-means clustering to achieve adaptive transfer of reference image colour features. Finally, a generative adversarial network integrating spatial feature transformation and full attention mechanism is constructed, which combines perceptual loss and adversarial loss to achieve visual perception driven colour restoration. The experimental results show that the proposed method has the lowest structural similarity of 0.93 and the highest index of 0.91, both of which are superior to existing comparison methods. Inderscience Publishers - linking academia, business and industry through research

Title: A colour restoration method for multi-texture oil painting images based on colour transfer

Authors: Xiaofeng Liu

Addresses: School of Fine Arts and Design, Mudanjiang Normal University, Mudanjiang, 157011, China

Abstract: Multi-texture oil painting images face challenges such as complex texture interference and artistic feature distortion in the process of colour restoration, making it difficult to accurately restore colour layers and maintain stroke details. Therefore, colour restoration method for multi-texture oil painting images based on colour transfer is proposed. Firstly, the low dimensional subspace denoising method combining RPCA and BM3D effectively removes image noise while preserving texture features. Secondly, design a colour transfer strategy that integrates brightness remapping and K-means clustering to achieve adaptive transfer of reference image colour features. Finally, a generative adversarial network integrating spatial feature transformation and full attention mechanism is constructed, which combines perceptual loss and adversarial loss to achieve visual perception driven colour restoration. The experimental results show that the proposed method has the lowest structural similarity of 0.93 and the highest index of 0.91, both of which are superior to existing comparison methods.

Keywords: colour transfer; transfer learning; oil painting images; colour restoration.

DOI: 10.1504/IJBIDM.2026.154232

International Journal of Business Intelligence and Data Mining, 2026 Vol.28 No.9, pp.117 - 132

Received: 31 Oct 2025
Accepted: 26 Feb 2026
Published online: 17 Jun 2026 *

Title: A colour restoration method for multi-texture oil painting images based on colour transfer

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