Title: Cloud tourism scene image processing technology based on K-means and image brightness enhancement algorithm

Authors: Xiaomei Sun

Addresses: School of Management, Zhengzhou University of Industrial Technology, Zhengzhou, 451100, China

Abstract: To improve segmentation accuracy and visual quality in cloud tourism images, this study proposes an enhanced framework combining a refined K-means algorithm and a DCGAN-based brightness enhancement network. K-means is improved using canny edge detection for clearer boundaries, maximum contour suppression to avoid misclassification in bright areas, and weighted cluster updates for better texture handling. Simultaneously, a convolutional block attention module is added to the DCGAN generator to emphasise critical spatial and channel features. Experiments on COCO and Cityscapes datasets yield segmentation accuracies of 98.53% and 98.04%, with PSNR reaching 33.4dB and SSIM at 0.93, confirming the method's effectiveness.

Keywords: K-means; DCGAN; image processing; cloud tourism; image segmentation; CBAM.

DOI: 10.1504/IJCC.2025.151118

International Journal of Cloud Computing, 2025 Vol.14 No.4, pp.371 - 389

Received: 29 Apr 2025
Accepted: 14 Jul 2025
Published online: 14 Jan 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article