RECONFIGURABLE PLATFORM BASED DESIGN IN FPGA FOR UNDERWATER IMAGE COLOR CORRECTION

Color correction is a serious constraint that comes into play in underwater imagery. Optical properties of light cause issues that can severely degrade the quality of underwater photography. As light with different energies travel through water, it is absorbed at higher rates, it contributes to severe bluishness of the underwater captured images. Thus it creates challenges for imaging in underwater. Attenuation of longer wavelengths occurs more rapidly when compared to shorter wavelengths, resulting overall blue color to the captured image. Scattering, independent of wavelength of visible light is one of the reasons for image loss and it does not influence color. In marine biology studies related to species identification and ecosystem monitoring and surveillance, examination of color is required. Color filters and manual post-processing are used for flooding objects with white light from a very close distance in underwater imaging techniques. However, these techniques are inefficient since it doesn’t render correct colors, and it works only for fixed setups, as loss of color depends upon distance. Using post processing methods for underwater images, underwater monitoring of natural and human-engineered environments can be sufficiently enhanced to a great extent. In this paper, hardware implementation of underwater image color correction is performed in Field Programmable Gate Array (FPGA) to be used in Automated Underwater Vehicles (AUV). FPGA platform is preferred as its ability to perform parallel algorithm such as image processing algorithms due to its inherent parallelism. INTRODUCTION Recovering process of underwater images involving retrieval of correct colors is a challenging task. This is due to the fact that illumination done for image capture is extremely altered in water since water is refractive and turbid medium. Relevant Image modifications are caused mainly due to following reasons: different attenuation of different wavelength, scattering and absorption of radiation by the underwater environment. For this task, many qualitative techniques based on imaging have been proposed in the literature. They are based on image restoration techniques or subjective techniques such as image enhancement methods. The information regarding medium physical parameters is essential in first case and image adjustments based on certain criterions are performed in second case. Gray world hypothesis is one of the most popular criteria in which average of the captured image is supposed to be gray. Implementing automatic methods based on this assumption yield good results. The color space consists of two opponent color components which encode α and β (yellow-blue and red-green chromaticities), and a luminance component (achromatic). This is applied for color discrimination in human color visual mechanism. With the help of lαβ color space, the chromatic components are relatively altered by displacing their distributions closer to the white point (white balancing). Histogram cut off and stretching of the luminance component is done to advance the contrast of the image. So, by performing a color correction similar to that in human visual system is the way to tackle the difficulties of underwater images based on color constancy. The average information in each image channel tends to gray that is captured color image averages to gray (achromatic). The main drawback of gray-world systems is that they will not reproduce correct colors if extremely huge number of colors is absent in the scene. The lαβ color space described by Ruderman et al, [1] examines the human eye insight of natural images. In human color visual mechanism, color space consists of a luminance component l and two adversary color constituents α and β. In applications like 3D imaging, documentation, navigation etc, color correction of underwater images or videos is a significant task. G. Biancoa et. al. [2] present a new method which deals with uniform illumination and gray-world assumptions used in the lαβ space for color correction. White balancing of chromatic components (αandβ) and l,the luminance element is processed to improve the contrast of the image. This procedure is appropriate in underwater imaging to eliminate unwanted color casts and in the gray shades, greenish-blue components are removed. Underwater monitoring can be improved by automatically capturing underwater color images using robots. In [3] adaptive illumination, a precise-color imaging method is used. A multi-color controllable light source is used to mix the illumination in a distance-dependent way. The color mix pays accurately for loss of color and produces an image and its composition of color which is alike to represent the object in air. Objects closer or far away from the camera will not be rendered appropriately because of the distance-dependent behavior of adaptive illumination. This work helps to capture color-accurate underwater images based on color which facilitates surveillance operations and automating underwater monitoring. Erik Reinhard and Tania [4] explain a universal technique for correction of color and it takes characteristics of color of one image from other. It describes that a color space with de-correlated axes is a helpful means for manipulating color images. Calculation of standard deviation and mean of the data points generates convincing output results providing suitable input images. The important approach is to select an appropriate color space to perform simple operations . The representation of an image (three channel) in any color spaces results in correlation between different channel values, which is not needed. Received: 9 Sept 2016 Accepted: 15 Oct 2016 Published: 18 Oct 2016 *Corresponding Author Email:alexrajsm@gmail.com Tel. : +91 471 230484