Emotion Detection From Distorted Images Using Optimized CNN & GAN

Image inpainting is a difficult process that entails visually coherently recreating areas of an image that are missing or contaminated. Producing realistic inpainting outcomes is a challenging task because of the intricacy of natural settings and the variety of image information. In this paper, a deep Convolutional Neural Network (CNN)-based system for context-aware image inpainting is presented, using the sophisticated object detection features of You Only Look Once version 5 (YOLOv5). YOLOv5 separates the image into grids, estimates the likelihood that each will need inpainting, and creates bounding boxes around any areas that are masked or damaged. A CNN-based generative model that uses the surrounding facial features to rebuild the missing content is then applied to these regions. PiiGAN, a generative adversarial network developed especially for photorealistic image inpainting with excellent semantic comprehension, is incorporated to improve inpainting quality. On test data, the trained model performed well, recognizing and correctly inpainting facial regions with a mean average precision (mAP) of 79%. The proposed method also holds potential for use in archaeology, where it can assist in digitally reconstructing damaged sculptures and supporting restoration of historical imagery.