Menstrual cycle inspired latent diffusion model for image augmentation in energy production

Area of Science:

• Energy production
• Artificial Intelligence
• Computer Vision

Background:

• Image classification is vital for energy sector operations.
• Latent diffusion models (LDMs) show promise for image augmentation but face challenges like pixel integrity and mode collapse.

Purpose of the Study:

• To introduce a novel framework, the menstrual cycle-inspired latent diffusion model (MCI-LDM), to address limitations in LDM-based image augmentation for the energy sector.

Main Methods:

• Integration of a menstrual cycle-inspired metaheuristic algorithm for pixel integrity.
• Employment of an adaptive attention mechanism for preserving fine details.
• Incorporation of a multi-scale feature enhancement module to mitigate mode collapse and improve texture consistency.

Main Results:

• MCI-LDM demonstrated superior performance in image augmentation, diversity, and generation accuracy across five energy-related datasets.
• Achieved higher Inception Score (7.1 vs. 5.4) and lower Fréchet Inception Distance (22.5 vs. 35.2) compared to LDM.
• Showcased improved image integrity with higher PSNR (32.7 dB vs. 28.5 dB) and SSIM (0.92 vs. 0.78).

Conclusions:

• MCI-LDM offers significant improvements over standard LDMs for image classification and data augmentation in the energy sector.
• The proposed model effectively enhances image quality, diversity, and structural coherence, making it a valuable tool for energy applications.