Mahmoud Gamal M, Elbaz Mostafa, Alqahtani Fayez, Alginahi Yasser, Said Wael
Department of Electrical Engineering, Pharos University in Alexandria, Alexandria, Egypt.
Department of Computer Science, Faculty of Computers and Informatics, Kafrelsheikh University, Kafrelsheikh, Egypt.
Sci Rep. 2024 Oct 13;14(1):23936. doi: 10.1038/s41598-024-73976-7.
Missing pixel imputation presents a critical challenge in image processing and computer vision, particularly in applications such as image restoration and inpainting. The primary objective of this paper is to accurately estimate and reconstruct missing pixel values to restore complete visual information. This paper introduces a novel model called the Enhanced Connected Pixel Identity GAN with Neutrosophic (ECP-IGANN), which is designed to address two fundamental issues inherent in existing GAN architectures for missing pixel generation: (1) mode collapse, which leads to a lack of diversity in generated pixels, and (2) the preservation of pixel integrity within the reconstructed images. ECP-IGANN incorporates two key innovations to improve missing pixel imputation. First, an identity block is integrated into the generation process to facilitate the retention of existing pixel values and ensure consistency. Second, the model calculates the values of the 8-connected neighbouring pixels around each missing pixel, thereby enhancing the coherence and integrity of the imputed pixels. The efficacy of ECP-IGANN was rigorously evaluated through extensive experimentation across five diverse datasets: BigGAN-ImageNet, the 2024 Medical Imaging Challenge Dataset, the Autonomous Vehicles Dataset, the 2024 Satellite Imagery Dataset, and the Fashion and Apparel Dataset 2024. These experiments assessed the model's performance in terms of diversity, pixel imputation accuracy, and mode collapse mitigation, with results demonstrating significant improvements in the Inception Score (IS) and Fréchet Inception Distance (FID). ECP-IGANN markedly enhanced image segmentation performance in the validation phase across all datasets. Key metrics, such as Dice Score, Accuracy, Precision, and Recall, were improved substantially for various segmentation models, including Spatial Attention U-Net, Dense U-Net, and Residual Attention U-Net. For example, in the 2024 Medical Imaging Challenge Dataset, the Residual Attention U-Net's Dice Score increased from 0.84 to 0.90, while accuracy improved from 0.88 to 0.93 following the application of ECP-IGANN. Similar performance enhancements were observed with the other datasets, highlighting the model's robust generalizability across diverse imaging domains.
缺失像素插补在图像处理和计算机视觉中是一项关键挑战，尤其是在图像恢复和修复等应用中。本文的主要目标是准确估计和重建缺失的像素值，以恢复完整的视觉信息。本文介绍了一种名为具有中智学的增强连接像素身份生成对抗网络（ECP-IGANN）的新型模型，该模型旨在解决现有用于生成缺失像素的生成对抗网络（GAN）架构中固有的两个基本问题：（1）模式崩溃，这会导致生成像素缺乏多样性；（2）在重建图像中保留像素完整性。ECP-IGANN纳入了两项关键创新来改进缺失像素插补。首先，在生成过程中集成了一个身份块，以促进保留现有像素值并确保一致性。其次，该模型计算每个缺失像素周围8连接的相邻像素的值，从而增强插补像素的连贯性和完整性。通过在五个不同数据集上进行广泛实验，对ECP-IGANN的有效性进行了严格评估：BigGAN-ImageNet、2024医学成像挑战数据集、自动驾驶车辆数据集、2024卫星图像数据集和2024时尚与服装数据集。这些实验从多样性、像素插补准确性和模式崩溃缓解方面评估了模型的性能，结果表明在Inception得分（IS）和Fréchet Inception距离（FID）方面有显著改进。ECP-IGANN在所有数据集的验证阶段显著提高了图像分割性能。对于各种分割模型，如空间注意力U-Net、密集U-Net和残差注意力U-Net等关键指标，如骰子系数、准确率、精确率和召回率都有大幅提高。例如，在2024医学成像挑战数据集中，应用ECP-IGANN后，残差注意力U-Net的骰子系数从0.84提高到0.90，而准确率从0.88提高到0.93。在其他数据集上也观察到了类似的性能提升，突出了该模型在不同成像领域的强大通用性。
