Abstract: Suzhou gardens are renowned for their unique color palettes and rich cultural significance. This study introduces a deep learning-optimized Contrast Limited Adaptive Histogram Equalization (CLAHE) method to enhance image contrast and improve color extraction accuracy in Suzhou garden images. An initial collection of 18,502 images was refined to 11,526 high-quality images from a single dataset. A pre-trained VGG16 convolutional neural network was used to extract image features, which were then employed to dynamically optimize the CLAHE parameters, thereby preserving the original color tones while enhancing contrast. The optimized CLAHE achieved significant improvements in the Structural Similarity Index (SSIM) by 24.69 percent and in the Peak Signal-to-Noise Ratio (PSNR) by 24.36 percent, and a reduction in Loss of Edge (LOE) by 36.62 percent,compared to the standard CLAHE. Additionally, enhanced structural detail and color complexity were observed. High-Resolution Network (HRNet) was utilized for semantic segmentation, enabling precise color feature extraction. K-means clustering was used to identify key color characteristics and complementary relationships among the primary and secondary colors in Suzhou gardens. A mathematical model capturing these relationships was developed to form the basis of a color palette generator, which can be applied to digital archiving, cultural preservation, aesthetic education, and virtual reality.

Chuanyuan Li and Ziyun Jiao, “Deep Learning-Optimized CLAHE for Contrast and Color Enhancement in Suzhou Garden Images” International Journal of Advanced Computer Science and Applications(IJACSA), 15(12), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151281

@article{Li2024,
title = {Deep Learning-Optimized CLAHE for Contrast and Color Enhancement in Suzhou Garden Images},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151281},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151281},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {12},
author = {Chuanyuan Li and Ziyun Jiao}
}