Image Color Balance with Laplacian and Gaussian Pyramid (CBLGP) Algorithm

Abstract

In recent times, technology has rapidly and significantly evolved across all sectors. Digital image processing stands out as a modern technology aimed at achieving clear images. However, digitized images often encounter issues of low quality, such as unclear or underwater images that require enhancement for better visibility. These problems stem from factors like deficient focusing, lighting, and various constraints leading to low contrast, shading, and artifacts. Underwater and satellite images consistently face less-than-ideal conditions due to environmental factors like light refraction in water, particle scattering, and dust in aquatic environments. Similarly, challenges in space, such as poor illumination and lack of contrast, further complicate image analysis. Overcoming these obstacles is crucial for extracting valuable information, necessitating advanced processing techniques. This paper introduces an enhanced Gaussian/Laplace color balance-fusion algorithm designed to improve image visibility. Modifications to certain equations result in sharper and clearer images. The algorithm begins by determining the white balance of the input RGB color image and subsequently enhances its intensity. Edge improvement is carried out separately using a depth filter. The weights for each image are then determined and combined to form a Laplace Pyramid. A color restoration technique is applied to process the resulting image, producing the final enhanced image. While existing methods for image contrast enhancement typically focus on image features, they often neglect user characteristics. This paper explores the application of image sharpening, a prominent image enhancement technique, in clearing underwater or low-quality images using the proposed algorithm. Keywords: EDSHE, High pass filter, White patch ratix, Laplacian pyramid, Color restoration.