Novel GSIP: GAN-based sperm-inspired pixel imputation for robust energy image reconstruction.

Area of Science:

• Computer Vision
• Machine Learning
• Image Processing

Background:

• Missing pixels in images degrade performance in tasks like segmentation and object detection.
• Generative Adversarial Networks (GANs) are powerful tools for image generation but face challenges like vanishing gradients and mode collapse.

Purpose of the Study:

• To develop a novel Generative Adversarial Network (GAN) approach for effective missing pixel imputation.
• To enhance the accuracy and coherence of imputed pixels in degraded images.

Main Methods:

• Proposed a new GAN architecture with an identity module to combat vanishing gradients.
• Integrated a sperm motility-inspired metaheuristic algorithm for optimal selection of neighboring pixels during imputation.
• Implemented an adaptive interval mechanism to improve generator efficiency and pixel coherence.

Main Results:

• Demonstrated superior performance in maintaining pixel integrity across three distinct datasets (Energy Images, NREL Solar Images, NREL Wind Turbine Dataset).
• Effectively addressed common GAN challenges, including mode collapse and vanishing gradients.
• Validated the approach's robustness across various GAN architectures.

Conclusions:

• The proposed GAN-based imputation method significantly improves the reconstruction of missing pixel data.
• The novel heuristic and architectural modifications enhance the reliability and efficiency of image imputation.
• This approach offers a robust solution for image processing tasks affected by missing pixel data.