Arbitrary style transfer via content consistency and style consistency

Yu, Xiaoming; Zhou, Gan

doi:10.1007/s00371-023-02855-5

Cited by 6 publications

(1 citation statement)

References 29 publications

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“…Fernandez et al introduced style migration into the field of reinforcement learning, and utilised the Neural Policy Style Migration algorithm (NPST) [16] to achieve style migration to the field of intelligent robotics, but the model is not applicable to static mural effects. Yu et al added the Covariance Attention Network (CovAttN) [17] to achieve stylistic uniformity between different patches of the image in terms of stylistic fusion, but stylistic confusion occurs when facing mural paintings where styles may be misaligned, which affects the final generation effect. Li et al proposed a two-branch feature processing module to enhance the overall features of the image [18] and preserve the object contours and an interpolation module with spatial awareness to achieve semantic consistency of the generated content, but texture disappearance occurs when using murals with darker stylistic colours and fuzzy texture details.…”

Section: Image Style Migrationmentioning

confidence: 99%

Feature Separation and Fusion to Optimise the Migration Model of Mural Painting Style in Tombs

Wu,

Li,

Zhang

2024

Applied Sciences

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Tomb murals are different from cave temple murals and temple murals, as they are underground cultural relics, their painting style is unique, solemn, and austere, and the performance image is characterised by simple colours, low contrast, and fewer survivors. During the digital restoration process, it is important to have sufficient reference samples to ensure the accuracy of the restoration. In addition, the style of mural paintings in the tombs varies greatly from other styles of murals and types of word paintings. Therefore, learning the unique artistic style of tomb murals, providing stylistically consistent training samples for digital restoration, and overcoming the problems of dim lighting and complex surface granularity of tomb murals are all necessary for research. This paper proposes a generative adversarial network algorithm that separates and fuses style features to enhance the generative network’s ability to acquire image information. The algorithm extracts underlying and surface style feature details of the image to be tested and conducts fusion generation experiments. The generative network’s parsing layer modifies the input noise tensor and optimises the corresponding weights to prevent misalignment between drawing lines and fresco cracks. Finally, to optimise the fresco generation effect, we add the corresponding loss function in the discriminator. The tomb murals dataset was established for experiments and tests, and quantitatively and qualitatively analysed with other style migration models, and SSIM, FID, LPIPS and NIQE were used as evaluation indexes. The results were 0.97, 269.579, 0.425 and 3.250, respectively, and the effect of style migration of this paper’s method was significantly higher than that of the control group model.

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Section: Image Style Migrationmentioning

confidence: 99%