Review of Undersea Image Enhancement by Wavelet Based Fusion

An underwater image becomes unclear due to many undersea atmosphere effects. The effects are influenced by suspended sediments that lead to light dispersion and absorption while formation of an image. Under water medium is not good to process image data. It leads to reduced color disappear and contrast issues. So, in image based exploration process, it is important to improve image data for future processing. Here, we present a wavelet-based fusion technique for underwater image improvement. Thus, it has been proved that publicly available unclear undersea images are enhanced and analyzed qualitatively using state of art methods.I. INTRODUCTION Currently, the automated research operations are done in deep sea.The immersed imaging is a vital aspect of these automated based events.The light is reduced in submerged habitat due to retention effects of water particles and other suspended contaminated particles.When light travels deep inside water extenuation increases.In such submerged environment during image formation process, less radiance extends to lenses from an object in site that changes original colors of an object.Perforation of visible spectrum colors depends on their wavelength and depth of water.Wavelengths that are long are absorbed first while wavelengths that are short stay at longer distance in water.In light transmission within water, photons strike into water molecules and other fragments.The collisions result in direction changes of photons, such kind of arbitrary change in photons direction influences light dispersion phenomenon.In many images, forward dispersion is accountable for contrast issues.Low contrast restricts visibility in underwater habitat and image development activity, these images will be dark.Low contrast issues affect feature extraction process in image processing methods.Wavelet filterbanks(FBs) have received substantial acceptance in image and signal processing applications, for ex-ample, denoising, compression, pattern recognition, water-marking etc. Filter-banks are categorized as bi-orthogonal or orthogonal.Bi-orthogonal (BO) Filter banks are chosen for image processing applications for linear phase criteria.It is advisable wavelet filters hold characteristics like flatness, regularity, high frequency selectivity etc. II.STATE OF THE ART Literature survey is the most important step in any kind of research.Before development starts we need to study previous papers of our domain which we are working and on the basis of study we can predict or generate the drawback and start working with the reference of previous papers."In this section, we briefly review the related work and their different techniques.For any image based exploration and inspection activity, it is essential to enhance the imaging data before going for further processing.This paper presents a wavelet-based fusion method to enhance the hazy underwater images by addressing the low contrast and color alteration issues.The publicly available hazy underwater images are enhanced and analyzed qualitatively with some state of the art methods [1].In this paper, they have introduced an effective technique to enhance the images captured underwater and degraded due to the medium scattering and absorption.The method is a single image approach that does not require specialized hardware or knowledge about the underwater conditions or scene structure.It builds on the blending of two images that are directly derived from a color compensated and whitebalanced version of the original degraded image.The two images to fusion, as well as their associated weight maps, are defined to promote the transfer of edges and color contrast to the output image.To avoid that the sharp weight map transitions create artifacts in the low frequency components of the reconstructed image, they also adapted a multiscale fusion strategy.Extensive qualitative and quantitative evaluation reveals that enhanced images and videos are characterized by better exposedness of the dark regions, improved global contrast, and edges sharpness.[2].In this paper, numbers of simple and inexpensive enhancement techniques are suggested.These techniques attempt to make use of easily computed local context, features to aid in the reassignment of each point's gray level during histogram transformation.[3].