Light emitting diode(LED) is the fourth generation lighting source,but it has some shortcomings such as complex chip packaging process and the unbalanced light color of phosphor in long-time application.In this study,...Light emitting diode(LED) is the fourth generation lighting source,but it has some shortcomings such as complex chip packaging process and the unbalanced light color of phosphor in long-time application.In this study,a kind of Eu-terephthalic acid/Tb-sulfosalicylate/ZrO_(2)/ZnZrO_(3)(Eu-PTA/Tb-SSA/ZrO_(2)/ZnZrO_(3))phosphor with warm white light emission properties was prepared,and the warm white light LED(wWLEDs) was successfully prepared by encapsulating Eu-PTA/Tb-SSA/ZrO_(2)/ZnZrO_(3) phosphors together with 270 nm UV-chip.The ZrO_(2)/ZnZrO_(3),Tb-SSA/ZrO_(2)/ZnZrO_(3) and Eu-PTA/ZrO_(2)/ZnZrO_(3) samples show blue emission,green emission and red emission under deep ultraviolet(UV,270 nm) excitation,respectively.The Tb-SSA and Eu-PTA are co-doped into ZrO_(2)/ZnZrO_(3) matrix with blue emission to achieve the warm white light emission,and the light color can be adjusted by controlling the doping amount of Eu^(3+)-and Tb^(3+).Through the excitation method of single-component phosphor by the single chip,the complex chip packaging process of w-LED can be solved.By doping rare earth organic complexes into porous ZrO_(2)/ZnZrO_(3) matrix,the problems of the light color unbalanced of phosphor and the low luminescence intensity of rare earth doped metal oxides composites can be solved.展开更多
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
Due to the different lighting environments or other reasons, the pixel colors may be quite different in one image which causes distinct visual discontinuities. It makes the analysis and processing of such an image mor...Due to the different lighting environments or other reasons, the pixel colors may be quite different in one image which causes distinct visual discontinuities. It makes the analysis and processing of such an image more difficult and sometime impossible. In this paper, a unified multi-toning image adjustment method is proposed to solve this problem. First, a novel unsupervised clustering method was proposed to partition the source and the target image into a certain number of subsets with similar color statistics. By matching the texture characteristics and luminance distribution between the blocks, it can create optimized correspondence. Then, the color information was transferred from the matched pixels in the source blocks to the target ones. Graph cut method was used to optimize the seams between different subsets in the final step. This method can automatically perform color adjustment of a multi-toning image. It is simple and efficient. Various results show the validity of this method.展开更多
基金supported by the National Natural Science Foundation of China (51572034)the Jilin Province Science and Technology Development Plan Project of China (20220203168SF)。
文摘Light emitting diode(LED) is the fourth generation lighting source,but it has some shortcomings such as complex chip packaging process and the unbalanced light color of phosphor in long-time application.In this study,a kind of Eu-terephthalic acid/Tb-sulfosalicylate/ZrO_(2)/ZnZrO_(3)(Eu-PTA/Tb-SSA/ZrO_(2)/ZnZrO_(3))phosphor with warm white light emission properties was prepared,and the warm white light LED(wWLEDs) was successfully prepared by encapsulating Eu-PTA/Tb-SSA/ZrO_(2)/ZnZrO_(3) phosphors together with 270 nm UV-chip.The ZrO_(2)/ZnZrO_(3),Tb-SSA/ZrO_(2)/ZnZrO_(3) and Eu-PTA/ZrO_(2)/ZnZrO_(3) samples show blue emission,green emission and red emission under deep ultraviolet(UV,270 nm) excitation,respectively.The Tb-SSA and Eu-PTA are co-doped into ZrO_(2)/ZnZrO_(3) matrix with blue emission to achieve the warm white light emission,and the light color can be adjusted by controlling the doping amount of Eu^(3+)-and Tb^(3+).Through the excitation method of single-component phosphor by the single chip,the complex chip packaging process of w-LED can be solved.By doping rare earth organic complexes into porous ZrO_(2)/ZnZrO_(3) matrix,the problems of the light color unbalanced of phosphor and the low luminescence intensity of rare earth doped metal oxides composites can be solved.
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.
基金Supported by Natural Science Foundation of China （61170118 and 60803047）, the Specialized Research Fund for the Doctoral Program of Higher Education of China （200800561045）
文摘Due to the different lighting environments or other reasons, the pixel colors may be quite different in one image which causes distinct visual discontinuities. It makes the analysis and processing of such an image more difficult and sometime impossible. In this paper, a unified multi-toning image adjustment method is proposed to solve this problem. First, a novel unsupervised clustering method was proposed to partition the source and the target image into a certain number of subsets with similar color statistics. By matching the texture characteristics and luminance distribution between the blocks, it can create optimized correspondence. Then, the color information was transferred from the matched pixels in the source blocks to the target ones. Graph cut method was used to optimize the seams between different subsets in the final step. This method can automatically perform color adjustment of a multi-toning image. It is simple and efficient. Various results show the validity of this method.
