International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 10 | Oct -2016 www.irjet.net p-ISSN: 2395-0072 © 2016, IRJET | Impact Factor value: 4.45 | ISO 9001:2008 Certified Journal | Page 180 Visual Enhancement of Undersea Images by ADCP Method Jasneet Kaur Babool 1 , Satbir Singh 2 , Saurabh Mahajan 3 1 M.Tech Scholar, Dept. of ECE, GNDU, RC, Gurdaspur, Punjab, India 2 Assistant Professor, Dept. of ECE, GNDU, RC, Gurdaspur, Punjab, India 3 Assistant Professor, Dept. of ECE, BECT, Gurdaspur, Punjab, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract – We have Purposed ADCP method for assessing perceptual image quality is a usual effort to quantify the perceptibility of inaccuracy between a vague image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. Key Words: MSE, PSNR, Image Processing, MATLAB, CLAHE, ADCP 1. INTRODUCTION The extensive distortions during acquisition, processing, compression, storage, transmission and reproduction may result in a degradation of visual quality of the digital images. For applications in which images are ultimately to be viewed by human beings, the only Dzaccuratedz method of quantifying visual image quality is through special evaluation, but is usually too inconvenient, costly and time-consuming. An objective image quality metric can play a variety of roles in image processing applications [1]. The examination of mention image quality assessment is to develop quantitative measures that can automatically predict perceived image quality i.e. it can be used to dynamically monitor and adjust image quality. Acquiring apparent images in undersea environments is a significant issue in ocean engineering. The quality of undersea images plays a pivotal role in scientific missions such as monitoring sea life, taking census of populations, and assessing geological or biological environments [2]. Undersea imaging is essential for scientific research and technology as well as for trendy activities, yet it is plagued by poor visibility environment. The most computer vision methods cannot be employed directly undersea [3]. This is due to the particularly challenging environmental conditions that complicate image matching and analysis. The optimization of undersea image system parameters predominantly focuses on maximizes signal strength and minimizes scattering effects. Obtaining satisfactory visibility of undersea objects has been historically difficult due to the absorptive and scattering properties of seawater. Mitigating these effects has been a long term research focus, but recent advancements in hardware, software, and algorithmic methods have led to noticeable improvement in system operational range. Acquiring clear images in undersea environments is an important issue in ocean engineering [3]-[4]. The quality of undersea images plays a pivotal role in scientific missions such as monitoring sea life, taking census of populations, and assessing geological or biological environments. The Color change and Light scattering are two main sources of deformation for undersea photography. The Color change corresponds to the varying degrees of attenuation encountered by light traveling in the sea with different wavelengths, rendering ambient undersea environments dominated by a bluish tone. The light incident on objects reflected and deflected several times by particles present in the water before getting the camera is due to light scattering [5]. This in turn lowers the visibility and contrast of the image captured. No existing undersea processing techniques can handle light scattering and color change distortions suffered by undersea images, and the possible presence of artificial lighting simultaneously. The Color change and Light scattering result in contrast loss and color deviation in images acquired undersea. Capturing images undersea is challenging, mostly due to mist caused by light that is reflected, deflected and scattered by water. The color change is caused by varying degrees of light attenuation for different wavelengths. The smog is due to suspended particles such as algae, sand, minerals, and plankton that exist undersea. As light reflected from objects propagates toward the camera, a portion of the light meets these suspended particles. This in turn absorbs and scatters the light beam. In the absence of blackbody radiation [6], the multi-scattering process along the course of propagation further disperses the beam into homogeneous background light. Conventionally, the processing of undersea images focuses solely on compensating either light scattering or color change distortion. Techniques targeting on removal of light scattering distortion include exploiting the polarization effects to compensate for visibility degradation [7], using image de-hazing to restore the clarity of the undersea images and combining point spread functions and a modulation transfer function to reduce the blurring effect [8]. Although the aforementioned approaches can enhance scene contrast and increase visibility, distortion caused by the