Image Enhancement using Multiple Image Processing Algorithms and Multiprocessor

For addressing typical image degradation problems including spots, haze, and blurriness, this research suggests an image enhancement framework that integrates patch-based inpainting, Dark Channel Prior (DCP) de-hazing, blurring, and sharpening techniques. As opposed to current algorithms that deal with each of these problems separately, the proposed approach provides a seamless workflow by utilizing multi-processors for faster processing and simultaneously addressing multiple degradation factors to enhance overall image quality. This integration improves visibility under challenging conditions, leading to better visual and quantitative results. The proposed method is widely applicable across various fields. For instance, digital photography can effectively remove unwanted spots and improve visibility in hazy conditions, resulting in cleaner and sharper images. Remote sensing can assist in cloud removal and atmospheric correction of satellite imagery, enhancing environmental monitoring accuracy. Additionally, the framework proves valuable in surveillance under hazy weather conditions by enhancing visibility. Furthermore, it can serve as a beneficial pre-processing step for machine learning (ML) models that train on images as degraded images can adversely affect the learning process. By providing enhanced images through our proposed framework, we can help train the ML models effectively thus, improving the efficiency of these models.