Focus AI: Revolutionizing Image Processing with GANs for Super-Resolution, U-Net for Background Removal, ResNet50 and LSTM for Context Aware Captioning

Identifying new patterns and extracting meaningful information from images has gained significant importance across various domains, including medical diagnostics, traffic monitoring, construction, and agriculture. As image data continues to grow exponentially, efficient processing and analysis methods are essential. Researchers have proposed numerous images processing systems, exploring advanced algorithms to enhance accuracy and efficiency. Deep learning techniques, particularly pretrained models, have revolutionized this field by automating complex image processing tasks. Despite significant progress in image processing, challenges such as low-resolution image quality, unreliable background segmentation, and inadequate contextaware captions remain unresolved. Traditional methods often fail to provide scalable and efficient solutions for these tasks. This research introduces Focus AI, a deep learning- based platform that integrates Generative Adversarial Networks (GANs) for image super- resolution, U-Net (CNN) for background removal, and ResNet50 + LSTM for caption generation using the COCO dataset. These advanced architectures work together to improve the quality and relevance of processed images. Expected improvements include higher-resolution image outputs, enhanced segmentation accuracy, and more contextually relevant captions compared to traditional methods.