UNDERWATER IMAGE ENHANCEMENT USING LAPLACE DECOMPOSITION AND YOLOV2 OBJECT DETECTION

Underwater imaging poses unique challenges due to the attenuation, scattering, and color distortion of light as it travels through the aquatic environment. This paper presents an innovative approach for enhancing underwater images by combining Laplace decomposition and YOLOv2 object detection. First, Laplace decomposition is applied to separate the underwater image into its low-frequency and high-frequency components. This decomposition effectively isolates the structural details from the unwanted artifacts, such as color cast and blurriness, commonly found in underwater imagery. Next, YOLOv2, a state-of-the-art object detection algorithm, is employed to identify and classify objects within the enhanced image. By integrating object detection, the proposed method not only enhances image quality but also provides valuable context for understanding the underwater scene. in improving image clarity, reducing color distortion, and accurately detecting objects of interest. The combination of Laplace decomposition and YOLOv2 object detection offers a robust solution for enhancing underwater images, making it a valuable tool for underwater research, surveillance, and exploration applications.

Keywords: Artifacts removal, contrast enhancement, edge preservation, image enhancement, DWT decomposition, under-water images, Yolov2, Yolov2 Layers.