Open Access

ARTICLE

Optimizing 2D Image Quality in CartoonGAN: A Novel Approach Using Enhanced Pixel Integration

Stellar Choi¹, HeeAe Ko², KyungRok Bae³, HyunSook Lee², HaeJong Joo⁴, Woong Choi^5,*

1 Department of Software Application Virtual Reality, Kangnam University, Yongin, 16979, Republic of Korea
2 AI·SW Convergence Research Institute, Kangnam University, Yongin, 16979, Republic of Korea
3 MuhanIT Co., Ltd., Seoul, 07299, Republic of Korea
4 Department of Artificial Intelligence, Kangnam University, Yongin, 16979, Republic of Korea
5 Division of ICT Convergence Engineering, Kangnam University, Yongin, 16979, Republic of Korea

* Corresponding Author: Woong Choi. Email:

(This article belongs to the Special Issue: Practical Application and Services in Fog/Edge Computing System)

Computers, Materials & Continua 2025, 83(1), 335-355. https://doi.org/10.32604/cmc.2025.061243

Received 20 November 2024; Accepted 19 February 2025; Issue published 26 March 2025

Abstract

Previous research utilizing Cartoon Generative Adversarial Network (CartoonGAN) has encountered limitations in managing intricate outlines and accurately representing lighting effects, particularly in complex scenes requiring detailed shading and contrast. This paper presents a novel Enhanced Pixel Integration (EPI) technique designed to improve the visual quality of images generated by CartoonGAN. Rather than modifying the core model, the EPI approach employs post-processing adjustments that enhance images without significant computational overhead. In this method, images produced by CartoonGAN are converted from Red-Green-Blue (RGB) to Hue-Saturation-Value (HSV) format, allowing for precise adjustments in hue, saturation, and brightness, thereby improving color fidelity. Specific correction values are applied to fine-tune colors, ensuring they closely match the original input while maintaining the characteristic, stylized effect of CartoonGAN. The corrected images are blended with the originals to retain aesthetic appeal and visual distinctiveness, resulting in improved color accuracy and overall coherence. Experimental results demonstrate that EPI significantly increases similarity to original input images compared to the standard CartoonGAN model, achieving a 40.14% enhancement in visual similarity in Learned Perceptual Image Patch Similarity (LPIPS), a 30.21% improvement in structural consistency in Structural Similarity Index Measure (SSIM), and an 11.81% reduction in pixel-level error in Mean Squared Error (MSE). By addressing limitations present in the traditional CartoonGAN pipeline, EPI offers practical enhancements for creative applications, particularly within media and design fields where visual fidelity and artistic style preservation are critical. These improvements align with the goals of Fog and Edge Computing, which also seek to enhance processing efficiency and application performance in sensitive industries such as healthcare, logistics, and education. This research not only resolves key deficiencies in existing CartoonGAN models but also expands its potential applications in image-based content creation, bridging gaps between technical constraints and creative demands. Future studies may explore the adaptability of EPI across various datasets and artistic styles, potentially broadening its impact on visual transformation tasks.

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Supplementary Material

Supplementary Material File

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