Face Image Inpainting Using Context Encoders and Dynamically Initialized Mask

The problem of recovering missing data has garnered considerable attention due to its significance and challenges in recent times. In particular, the ability to recover clear face images from occluded face images has found applications in various domains. One prominent approach in this context is the utilization of autoencoders within the framework of Generative Adversarial Networks (GAN), such as the Context Encoder (CE). The CE is an unsupervised algorithm that leverages an autoencoder as its generator. It is designed to inpaint missing areas in an image based on the information present in the surrounding areas. By learning a compressed representation of the input image, the autoencoder can generate plausible and visually coherent predictions for the missing regions. We found that the initial values of the pixels in the missing area have a significant effect on the quality of the generated images. Careful selection of these initial values proved crucial in achieving accurate and visually appealing inpainted results. Furthermore, we explored various useful loss functions that can be employed within the model. We discovered that the choice of loss function also has a substantial effect on the visual quality of the generated images.