Exploring the Extremes of Two-Layer Convolutional Autoencoders Architecture in Image Inpainting

Inpainting, the process of filling in missing or damaged regions of an image, has witnessed significant progress with the advent of deep learning, particularly neural networks. This investigation delves into the outermost capabilities of two-layer Convolutional Autoencoders (2L-CAEs) architectures applied to image inpainting. The study scrutinizes the boundaries of performance and feasibility within this specific neural network configuration. The overarching objective is to provide an intricate understanding of the potential and limitations inherent in pushing a 2L-CAE architecture to its extremes, particularly when tasked with the intricate process of mending missing or impaired regions within an image.