under water image enhancement

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enhances under water images

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ENHANCEMENT OF UNDER WATER IMAGES AbstractThe underwater image processing area has received considerable attention within the last decades, showing important achievements. The underwater images are essentially characterized by their poor visibility because light is exponentially attenuated as it travels in the water and the scenes result poorly contrasted and hazy. When we go deeper,colors drop off one by one depending on their wavelenghth, blue color travels the longest in the water due to its shortest wavelength, making the underwater images to be dominated essentially by blue color. In summary,the images suffer from limited range visibility, low contrast, non uniform lighting, blurring, color diminished (bluish appearance) and noise. In order to improve the perception, underwater images are post processed to enhance the quality of the image.

In this project, a denoising method is used for removing additive noise present in the underwater images. Image denoising using the wavelet transform has been attracting much attention. Wavelet based approach provides a particularly useful method for image denoising when the preservation of image features in the scene is of importance. In the proposed denoising method, first homomorphic filtering is used for correcting non uniform illumination, and then anisotropic filtering is used for smoothing. After smoothing, wavelet subband threshold with Modified BayesShrink function is applied. The whole of the algorithm used in this project is to be coded in MATLAB. A GUI is to be developed in MATLAB to showcase the processed image after every step.

Dept Of E&C, NMAMIT

ENHANCEMENT OF UNDER WATER IMAGES CHAPTER 1 1.1 INTRODUCTIONUnderwater images are essentially characterized by their poor visibility because light is exponentially attenuated as it travels in the water and the scenes result poorly contrasted and hazy. Light attenuation limits the visibility distance at about twenty meters in clear water and five meters or less in turbid water. The light attenuation process is caused by absorption and scattering. The absorption and scattering process of the light in water influence the overall performance of underwater imaging systems. Forward scattering generally leads to blurring of the image features. On the other hand, backscattering generally limits the contrast of the images. Absorption and scattering effects are not only due to the water itself but also due to the components such as dissolved organic matter[4]. The visibility range can be increased with artificial illumination of light on the object but it produces non-uniform of light on the surface of the object and producing a bright spot in the center of the image with poorly illuminated area surrounding it. The amount of light is reduced when we go deeper, colors drop off depending on their wavelengths. The blue color travels the longest in the water due to its shortest wavelength[2]. Underwater image suffers from limited range visibility, low contrast, non-uniform lighting, blurring, bright artifacts, color diminished and noise. In this project, wavelet based image denoising technique is used for removing additive noise in the underwater images. Before applying the wavelet shrinkage function, first homomorphic filtering is used to correct non-uniform illumination of light. Homomorphic filter simultaneously normalizes the brightness across an image and increases contrast. The homomorphic filtering performs in the frequency domain and it adopts the illumination and reflectance model. After correcting non uniform illumination using homomorphic filtering, anisotropic filtering is used to smooth the image in homogeneous area but preserve image features and enhance them. Finally, applying wavelet denoising technique to denoise the image. Wavelet based image denoising techniques are necessary to remove random additive Gaussian noise while retaining as much as possible the important image features. The main objective of these types of random noise removal is to suppress the noise while preserving the original image details. Especially for the case of additive white Gaussian noise a number of techniques using wavelet-based thresholding have been proposed. In the recent years there has been a fair amount of research on wavelet thresholding and threshold selection for image

Dept Of E&C, NMAMIT

ENHANCEMENT OF UNDER WATER IMAGESdenoising, because wavelet provides an appropriate basis for separating noisy signal from the image signal.

1.2 Literature survey Problems in underwater imagesA major difficulty to process underwater images comes from light attenuation. Light attenuation limits the visibility distance, at about twenty meters in clear water and five meters or less in turbid water. The light attenuation process is caused by the absorption (which removes light energy) and scattering (which changes the direction of light path)[.

Absorption and scattering effects are due to the water itself and to other components such as dissolved organic matter or small observable floating particles[4]. Not only the amount of light is reduced when we go deeper but also colors drop off one by one depending on the wavelength of the colors. Red color disappears at the depth of 3m. Secondly, orange color starts disappearing while we go further. At the depth of 5m,the orange color is lost. Thirdly most of the yellow goes off at the depth of 10m and finally the green and purple disappear at further depth[2]. This is shown diagrammatically in Figure 1.1. The blue color travels the longest in the water due to its shortest wavelength. This is reason which makes the underwater images having been dominated only by blue color. In addition to excessive amount of blue color, the blur images contain low brightness and low contrast.

Figure 1.1 Color appearance in underwater In this project, a denoising method is used for removing additive noise present in the underwater images. Image denoising using the wavelet transform has been attracting muchDept Of E&C, NMAMIT

ENHANCEMENT OF UNDER WATER IMAGESattention. Wavelet based approach provides a particularly useful method for image denoising when the preservation of image features in the scene is of importance[6]. Wavelet analysis is an exciting new method for solving difficult problems in mathematics, physics, and engineering, with modern applications as diverse as wave propagation, data compression, signal processing, image processing, pattern recognition, computer graphics, the detection of aircraft and submarines and other medical image technology. Wavelets allow complex information such as music, speech, images and patterns to be decomposed into elementary forms at different positions and scales and subsequently reconstructed with high precision. Signal transmission is based on transmission of a series of numbers. The series representation of a function is important in all types of signal transmission. The wavelet representation of a function is a new technique. Wavelet transform of a function is the improved version of Fourier transform. Fourier transform is a powerful tool for analyzing the components of a stationary signal. But it is failed for analyzing the non stationary signal where as wavelet transform allows the components of a non-stationary signal to be analyzed. In 1982 Jean Morlet a French geophysicist, introduced the concept of a `wavelet'. The wavelet means small wave and the study of wavelet transform is a new tool for seismic signal analysis. Immediately, Alex Grossmann theoretical physicists studied inverse formula for the wavelet transform. The joint collaboration of Morlet and Grossmann yielded a detailed mathematical study of the continuous wavelet transforms and their various applications, of course without the realization that similar results had already been obtained in 1950's by Calderon, Littlewood, Paley and Franklin. However, the rediscovery of the old concepts provided a new method for decomposing a function or a signal.

Some Advantages of Wavelet Theorya) One of the main advantages of wavelets is that they offer a simultaneous localization in time and frequency domain. b) The second main advantage of wavelets is that, using fast wavelet transform, it is computationally very fast. c) Wavelets have the great advantage of being able to separate the fine details in a signal. Very small wavelets can be used to isolate very fine details in a signal, while very large wavelets can identify coarse details. d) A wavelet transform can be used to decompose a signal into component wavelets.

Dept Of E&C, NMAMIT

ENHANCEMENT OF UNDER WATER IMAGESe) In wavelet theory, it is often possible to obtain a good approximation of the given function f by using only a few coefficients which is the great achievement in compare to Fourier transform. f) It can often compress or de-noise a signal without appreciable degradation.

Dept Of E&C, NMAMIT

ENHANCEMENT OF UNDER WATER IMAGES Chapter 2 2.1 Objective of the project- To correct non uniform illumination and to enhance contrasts in the image. - To suppress noise which is inherent in underwater images. - To smooth the images while preserving the edges in the image. - Suppressing the predominant blue color by equalizing the RGB channels.

2.2 Block DiagramFigure 2.1 shows the block schematic of proposed project

Input Image

Conversion from RGB to YCbCr

Homomorphic Filtering

Anisotropic Filtering

Denoising

Conversion from YCbCr to RGB

Equalising Color Mean

Enhanced & denoised image

Figure 2.1 block schematic of proposed project

2.2.1 Conversion from RGB to YCbCrThis color space conversion allows us to work only on one channel instead of processing the three RGB channels. In YCbCr color space we process only the luminance channel (Y) corresp