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Under Water Image Haze Removal and Color Change Balance Using WCID AlgorithmB. Devi1, G.Lingeshwari22

PG scholar, The Rajas Engineering college, Vadakankulam Faculty of ECE department, The Rajas Engineering college, Vadakankulam devi.asm.abi@gmail.com,9976689693

1

Abstract Light scattering and color change are the major sources of distortion of underwater moving images. Light scattering is the source of haze. Light scattering is caused by means of water particles, the light incident on object gets reflected and deflected multiple times before reaching the camera. This in turn lowers the visibility of the captured moving images. When the light travelling in water with different wavelength, the light gets attenuated with different rates. By means of varying degree of attenuation, the moving images original color gets change. This project removes the haze and restores the color balance simultaneously using WCID algorithm. Initially the input moving images are separate in to N number of frames. By using WCID algorithm each frame is involved to a different process such as segmentation, removal of artificial light source, compensation of light scattering and compensation color change. Finally all N fames are combined together to form a haze free and color corrected moving images by frame to video converter. Key wordsColor change, Image dehazing, Light scattering,Underwater image, Wavelength compensation.I. INTRODUCTION

poor signal to noise returns, and the blurring caused by strong scattering due to water and its constituents which includes various sized properties. To properly address this issue, knowledge of in-water optical properties and their relationship to the image formation can be exploited in order to restore the image to the best possible level. B. Distortion on underwater image Underwater images raise new challenges and impose significant problems due to the following two big problems Light scattering effects Light attenuation effects Light scattering and color change result in contrast loss and color deviation in images acquired underwater. Capturing images in underwater is challenging, mostly due to haze caused by light that is reflected from a surface and is deflected and scattered by water particles. Haze is caused by suspended particles such as sand, minerals and plankton that exist in lakes, oceans, and rivers. As light reflected from objects propagates toward the camera, a portion of the light meets these suspended particles is shown in fig1. This in turn absorbs and scatters the light beam.

A. General description of underwater image Due to environmental conditions arising from different water types and associated in-water optical properties, it is very difficult to observe underwater environments with cameras. Capturing images in underwater is challenging, mostly due to haze caused by light that is reflected from a surface and is deflected and scattered by water particles, and color change due to varying degrees of light attenuation for different wavelengths. And also Capturing color in water is challenging due to the heavy non-uniform attenuation of light in water across the visible spectrum, which results in dramatic hue shifts toward blue. The quality of underwater images plays a pivotal role in scientific missions such as monitoring sea life, taking census of populations, and assessing geological or biological environments. Very little research has been carried out to process underwater images. The main challenge work underwater imagery results from the rapid decay of signals due to absorption, which leads to

Figure1. Light scattering effect Color change due to varying degrees of light attenuation for different wavelengths is shown in fig2. The term color refers to the red, green and blue values (often called the color channels) for each pixel in an image. Prominent blue color of clear ocean water, apart from sky reflection, is due to selective absorption by

water molecules. The time of day and cloudiness of the sky also have a great effect on the nature of the light available. Another factor is depth, once at sufficient depth, no amount of filtration can Effectively restore color loss. Due to the nature of underwater optics, red light diminishes when the depth increases, thus producing blue to grey like images. By 3m in depth there is almost no red light left from the sun. By 5m, orange light is gone, by 10m most yellow is also gone. By the time one reaches 25m only blue light remains. Since many (if not all) of the above factors are constantly changing, we cannot really know all the effects of water.

The following WCID algorithm provides the haze free and color corrected underwater moving images. The algorithm for wavelength compensation and image dehazing (WCID) techniques of WCID to remove distortions caused by light scattering and color change.

Figure 2. Different wavelengths of light are attenuated at different rates in water.

This project presents the algorithm for wavelength compensation and image dehazing (WCID) techniques of WCID to remove distortions caused by light scattering and color change. Dark-channel prior, an existing scene-depth derivation method, is used first to estimate the distances of the scene objects to the camera. Based on the depth map derived, the foreground and background areas within the image are segmented. The light intensities of foreground and background are then compared to determine whether an artificial light source is employed during the image acquiring process. If an artificial light source is detected, the luminance introduced by the auxiliary lighting is removed from the foreground area to avoid overcompensation in the stages followed. Next, the dehazing algorithm and wavelength compensation are utilized to remove the haze effect and color change along the underwater propagation path to the camera. Energy compensation for each color channel is carried out subsequently to adjust the bluish tone to a natural color. With WCID, expensive optical instruments or stereo image pairs are no longer required. WCID can effectively enhance visibility and restore the color balance of underwater images, rendering high visual clarity and color fidelity.II PROJECT DESCRIPTION

Figure.3 Flowchart of the WCID AlgorithmB. Separation of Frames from moving images

The input moving images are separated into different number of frames according to the height, width and number of frames choosen. Each and every frames involved to a compensation of light scattering process and compensation of color change process. These process are repeated upto the last frame choosen. C. Under water image model The background light in an underwater image can be used to approximate the true in-scattering term in the full radiative transport equation to achieve the following simplified hazy image formation mode I(x) = J(x).t(x)+(1-t(x)).B {red,green,blue} (1) where t(x) = Nrer() where x is a point in the underwater scene, is the image captured by the camera, is the scene radiance at point x, is the residual energy ratio of after reflecting from point in the underwater scene and reaching the camera, is the homogeneous background light, and is the light wavelength. Summarizes the overall effects for both light scattering and color change suffered by light with wavelength traveling the underwater distance . The residual

A. Flow chart of WCID algorithm

energy ratio [Nrer()] can be represented alternatively as the energy of a light beam with wavelength before and after traveling distance within the water .

camera. During both forward and backward courses of propagation pertinent to , color change occurs. I(x)=( (x).Nrer()D(x)+ rer()d(x))

Nrer()=

(x)). rer()d(x)+ +(1- Nrer()d(x)).B {red,green,blue} (3)

At point , the light reflected again travels distance to the camera forming pixel , red, green, blue . Along this underwater object camera path, two phenomena occur, i.e., light scattering and color change. Note that color change occurs not only along the surfaceobject propagation path but also along the object camera route. Light emanated from point is equal to the amount of illuminating ambient light reflected, i.e., Nrer, where is the reflectivity of point for light with wavelength. By following the image formation model in a hazy environment in (1), the image formed at the camera can be formulated as follows I(x)=( (x).Nrer ()D(x). (x)).Nrer()d(x)

Once the scene depth, i.e., object camera distance , is known through the dark-channel prior, the value of residual energy ratio Nrer after wavelength attenuation can be calculated; thus, the direct attenuation term Nrer is derivable through a dehazing procedure. The surface object distance is calculated by comparing the residual energy ratio of different color channels. Given the water depth , the amount of reflecting light

+ (1 - Nrer()d(x)).B ,

{red, green,

blue},

(2)

Equation (2) incorporates light scattering during the course of propagation from object to the camera, and the wavelength attenuation along both the surface object path and object camera route , Once the scene depth, i.e., object camera distance , is known through the dark-channel prior, the value of residual energy ratio Nrer after wavelength attenuation can be calculated; thus, the direct attenuation term Nrer is derivable through a dehazing procedure. The surface object distance is calculated by comparing the residual energy ratio of different color channels. Given the water depth , the amount of reflecting light , i.e., free of light scattering and color change, from point illuminated by airlight is determined. Moreover, the artificial light source is often provided to overcome insufficient lighting commonly encountered in an underwater photographic environment. The luminance contributed by