Novel GSIP: GAN-based Sperm-Inspired Pixel Imputation for Robust Energy Image Reconstruction

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 the filtration process 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 key 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 real 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 common challenges in GANs, such as mode collapse and vanishing gradients, across various GAN architectures.