Previous research utilizing Cartoon Generative Adversarial Network(CartoonGAN)has encountered limitations in managing intricate outlines and accurately representing lighting effects,particularly in complex scenes requ...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.展开更多
基金supported by the National Research Foundation of Korea(NRF)under Grant RS-2022-NR-069955(2022R1A2C1092178).
文摘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.
