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.

Missing pixel imputation is a critical task in image processing, where the presence of high percentages of missing pixels can significantly degrade the performance of downstream tasks such as image segmentation and object detection. This paper introduces a novel approach for missing pixel imputation based on Generative Adversarial Networks (GANs). We propose a new GAN architecture incorporating an identity module and a sperm motility-inspired heuristic during filtration to optimize the selection of pixels used in reconstructing missing data. The intelligent sperm motility heuristic navigates the image’s pixel space, identifying the most influential neighboring pixels for accurate imputation. Our approach includes three essential modifications: (1) integration of an identity module within the GAN architecture to mitigate the vanishing gradient problem; (2) introduction of a metaheuristic algorithm based on sperm motility to select the top 10 pixels that most effectively contribute to the generation of the missing pixel; and (3) the implementation of an adaptive interval mechanism between the discriminator’s actual value and the weighted average of the selected pixels, enhancing the generator’s efficiency and ensuring the coherence of the imputed pixels with the surrounding image context. We evaluate the proposed method on three distinct datasets (Energy Images, NREL Solar Images, and NREL Wind Turbine Dataset), demonstrating its superior performance in maintaining pixel integrity during the imputation process. Our experiments also confirm the approach’s effectiveness in addressing everyday challenges in GANs, such as mode collapse and vanishing gradients, across various GAN architectures.

Integrating energy and solar imagery is essential for electrical engineers in renewable energy prediction, consumption analysis, regression modeling, and fault detection applications. A significant challenge in these areas is the limited availability of high-quality datasets, which can hinder the accuracy of the predictive models. To address this issue, this paper proposes leveraging Generative Adversarial Networks (GANs) to generate synthetic samples for training. Despite their potential, traditional GAN face challenges such as mode collapse, vanishing gradients, and pixel integrity issues. This paper introduces a novel architecture, Penca-GAN, which enhances GANs through three key modifications: (1) dual loss functions to ensure pixel integrity and promote diversity in augmented images, effectively mitigating mode collapse and improving the quality of synthetic data; (2) the integration of an identity block to stabilize training, preserving essential input features and facilitating smoother gradient flow; and (3) a pancreas-inspired metaheuristic loss function that dynamically adapts to variations in training data to maintain pixel coherence and diversity. Extensive experiments on three renewable energy datasets—SKY images, Solar images, and Wind Turbine images—demonstrate the effectiveness of the Penca-GAN architecture. Our comparative analysis revealed that Penca-GAN consistently achieved the lowest Fréchet Inception Distance (FID) scores (164.45 for SKY, 113.54 for Solar, and 109.34 for Wind Turbine), indicating superior image quality compared to other architectures. Additionally, it attains the highest Inception Score (IS) across all datasets, scoring 71.43 for SKY, 87.65 for Solar, and 90.32 for Wind Turbine. Furthermore, the application of Penca-GAN significantly enhanced the fault detection capabilities, achieving accuracy improvements from 85.92 to 90.04% for solar panels and from 86.06 to 90.43% for wind turbines. These results underscore Penca-GAN’s robust performance in generating high-fidelity synthetic images, significantly advancing renewable energy applications, and improving model performance in critical tasks such as fault detection and energy prediction.

This paper presents MCI-GAN, a novel menstrual cycle imputation (MCI) and generative adversarial network (GAN) framework designed to address the challenge of missing pixel imputation in medical images. Inspired by the intelligent behavior of the endometrial lining during the menstrual cycle, our method introduces four key innovations. First, we propose a novel metaheuristic algorithm that assigns weights to surround pixels based on menstrual cycle behavior, ensuring that the imputed pixels maintain structural integrity and coherence with their neighbors, thus preserving overall image quality. Second, to enhance the learning capability of the GAN, identity blocks are integrated into the network architecture, improving the network’s ability to capture complex spatial relationships and leading to more accurate and consistent imputation of missing pixels. Third, we introduce an adaptive loss function that dynamically adjusts the penalty for pixel discrepancies based on local image context, allowing the model to focus on areas where accurate imputation is most critical and thereby enhancing overall image fidelity. Fourth, the framework incorporates a multi-scale feature extraction mechanism, enabling the GAN to process and combine information at various levels of detail, ensuring that both fine-grained textures and larger structural patterns are accurately captured during the imputation process. The efficacy of MCI-GAN is demonstrated across three diverse medical imaging datasets: mammograms, magnetic resonance imaging (MRI) scans, and skin lesion images. Our results show that the proposed method significantly outperforms existing approaches in terms of accuracy and structural coherence, offering a robust solution for missing pixel imputation in medical imaging.

Cellular classification is essential for understanding biological processes and disease mechanisms. This paper introduces a novel approach that employs two complementary loss functions within a Generative Adversarial Network (GAN) framework for processing images from the Human Protein Atlas dataset. Our method introduces the “Salivary Gland” loss function (SG-Loss), which addresses missing pixel imputation through a unique computational mechanism that models the graded secretion patterns of acinar cells, incorporating multi-scale contextual information to reconstruct incomplete cellular features. This is paired with our innovative “Pituitary Gland” loss function (PG-Loss), which preserves structural integrity through a novel homeostatic regularization approach that adaptively weights pixel relationships based on subcellular compartment boundaries, unlike conventional smoothing techniques. The SG-Loss specifically targets discontinuities in protein expression patterns, while PG-Loss maintains biological plausibility by enforcing organelle-specific constraints learned from annotated training data. Our proposed Dual-Gland GAN demonstrates superior performance with an Inception Score of 9.83 (± 0.31) and MS-SSIM Diversity of 0.187 (± 0.021). The model achieves impressive precision and recall metrics (0.872 and 0.835, respectively), resulting in an F1-score of 0.853. Training stability is reflected in minimal generator and discriminator loss variance (0.028 and 0.032) with convergence achieved in 78 epochs. Comprehensive evaluation shows high quality and diversity scores (0.912 and 0.894), yielding a combined score of 0.903, demonstrating the effectiveness of our biologically inspired approach for cellular image generation and classification. The results also prove the efficiency of the architecture in enhancing the classification results.

In the energy production domain, image classification is critical for monitoring, diagnostics, and operational optimization tasks. Latent diffusion models (LDMs) have shown potential in generating diverse images during the augmentation process based on text input. However, they are hindered by pixel integrity, texture consistency, and mode collapse. This paper introduces menstrual cycle-inspired latent diffusion model (MCI-LDM), a novel framework that addresses these challenges with three key modifications. First, a menstrual cycle-inspired metaheuristic algorithm is integrated to improve generated images’ pixel integrity and structural coherence. Second, an adaptive attention mechanism is employed to dynamically focus on critical regions during image generation, ensuring that fine details are preserved. Third, a multi-scale feature enhancement module is incorporated to capture global structures and local textures, mitigating mode collapse and enhancing overall image quality. Extensive experiments were conducted on five energy-related datasets, demonstrating the superior performance of MCI-LDM in terms of image augmentation, diversity, and generation accuracy. The results highlight the efficiency of the proposed model, making it a valuable tool for improving image classification and data augmentation in energy sector applications. MCI-LDM outperforms LDM by generating more diverse images, with a higher Inception Score (7.1 vs. 5.4) and a lower Fréchet Inception Distance (22.5 vs. 35.2), indicating better quality and variation. Additionally, MCI-LDM preserves image integrity more effectively, achieving superior PSNR (32.7 dB vs. 28.5 dB) and SSIM (0.92 vs. 0.78).