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De-Noising and Contrast Enhancement Using Bilateral Filter and Adaptive Histogram Equalization E

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De-Noising and Contrast Enhancement Using Bilateral Filter and Adaptive Histogram Equalization E ISSN 2319-8885 Vol.06,Issue.24 July-2017, Pages:4614-4619 www.ijsetr.com De-noising and Contrast Enhancement using Bilateral Filter and Adaptive Histogram Equalization E. GOPALA RAO1, Y. PAVAN KUMAR2 1PG Scholar, Dept of ECE, Andhra Loyola Institute of Engineering and Technology, Vijayawada, AP, India, Email: [email protected]. 2Assistant Professor, Dept of ECE, Andhra Loyola Institute of Engineering and Technology, Vijayawada, AP, India, Email: [email protected]. Abstract: Image enhancement is a region of improving the visual clarity of the image in digital image processing. In this paper propose a new algorithm using CLAHE and unsharp masking with bilateral filter. Enhancement of contrast and sharpness of an image is required in many applications. As one of the most common weather conditions, fog has whitening effect on the scenery, drops the atmospheric visibility, which leads to the decline of image contrast, gained by optical equipment, and produces fuzzily to the image. The scenery image gained in the foggy weather is not clear visually. Since it can be seen that low gray value is strengthened, the high gray value is weaken, leading to the over-concentrated distribution of pixel gray value, which is an obvious contrast degradation problem, therefore the foggy day image's restoration can be regarded as the image contrast enhancement problem. Firstly, carry on the Dual-Tree complex wavelet decomposition to the image, and then obtain the low frequency component and high-frequency components of image, use the Bilateral filter to low-frequency component, while utilize soft threshold based on level dependent threshold estimation to process high-frequency components, after that in proposal, principal feature (eigen value) will be separated from low frequency and it is modified with certain enhancement factor and eventually carry on wavelet restructuring to the processed components. Finally the simulated results shows that used approaches provides better accuracy in image contents preservation with high signal to noise ratio rather than exist methods. Keywords: CLAHE, DTCWT, Bilateral Filter. I. INTRODUCTION The limitation of digital cameras in capturing real scenes The real world scenes have a very wide range of luminance with large lightness dynamic range, a category of image levels. But in the field of photography, the ordinary cameras acquisition and processing techniques, collectively named are not capable of capturing the true dynamic range of a High Dynamic Range Imaging, gained popularity. To natural scene. To enhance the dynamic range of the captured acquire HDR scenes, consecutive frames with different image, a technique known as High Dynamic Range (HDR) exposures are typically acquired and combined into a HDR imaging is generally used. HDR imaging is the process of image that is viewable on regular displays and printers. capturing scenes with larger intensity range than what Image Enhancement is essentially a simplest and attractive conventional sensors can capture. It can faithfully capture area of digital image processing. Image enhancement is the details in dark and bright part of the scene. If the bright method used to enhance the overall superiority of the and dark regions coexist in the same scene, these regions corrupted images can be attained by using enhancement tend to be under- or over-saturated. In order to overcome the mechanisms. So that the human eye can naturally detect the limited dynamic range, high dynamic range (HDR) imaging key features of the pictures. It is used to eliminate. the has been introduced, and the capability of taking images in inappropriate artifacts from the pictures like noise or extremely HDR scenes has become necessary for a modern brighten the photograph and it simply to identify main digital still camera (DSC). Since digital imaging sensors, features and then it looks improved. It is an individual area such as charge coupled devices, has lower dynamic range of digital image processing. To create a graphic display than analog negative films, DSC highly relies on the auto further helpful to visualize and examination, it recover the exposure (AE) control function to determine the right photograph features such as edges or boundaries. It enlarges exposure value for covering the maximum dynamic range of the dynamic range of collected features. the scene being taken. High Dynamic Range (HDR) imaging techniques have been utilized in recent years as an II. EXISTING METHOD alternative approach for digital imaging. The below figure 1 shows the block diagram of existing system. Conversion of Low Dynamic Range images to High Copyright @ 2017 IJSETR. All rights reserved. E. GOPALA RAO, Y. PAVAN KUMAR Dynamic Range images by Local Histogram Separation "value" or "brightness". Note that while "hue" in HSL and (LHS) method and edge- preserving spacially adaptive de- HSV refers to the same attribute, their definitions of noising. "saturation" differ dramatically. Because HSL and HSV are simple transformations of device-dependent RGB models, the physical colors they define depend on the colors of the red, green, and blue primaries of the device or of the particular RGB space, and on the gamma correction used to represent the amounts of those primaries. As a result, each unique RGB device has unique HSL and HSV absolute color spaces to accompany it (just as it has unique RGB absolute color space to accompany it), and the same numerical HSL or HSV values (just as numerical RGB values) may be displayed differently by different devices. Both of these representations are used widely in computer graphics, and one or the other of them is often more convenient than RGB, Fig 1. Diagram of existing method. but both are also criticized for not adequately separating color-making attributes, or for their lack of perceptual The weighted histogram separation (WHS) is constructed uniformity. Other more computationally intensive models, based on the data separation units (DSUs), which separates such as CIELAB or CIECAM02, are said to better achieve the dataset into two subsets. Let H be a dataset and H(p) be these goals. the p-th value of dataset. For example, H can be represented III. PROPOSED METHOD to the luminance histogram of the gray level image. The first Fuzzy Fusion Based High Dynamic Range Imaging using step of the DSU is to estimate the threshold IJ, and it is Adaptive Histogram Separation and Contrast limited defined as, Adaptive Histogram Equalization methods. The block diagram of the proposed method was shown in below figure 2. However, AHE has a tendency to overamplify noise in relatively homogeneous regions of an image. A variant of adaptive histogram equalization called contrast limited Where w is a weighted factor to control the ratio of two adaptive histogram equalization (CLAHE) prevents this by separated subsets’ size. The variable M denotes the amount limiting the amplification. Ordinary histogram equalization of the dataset, according to, uses the same transformation derived from the image histogram to transform all pixels. This works well when the distribution of pixel values is similar throughout the image. Where H is the dataset dimension, that is |H|=H. For the However, when the image contains regions that are luminance histogram, H is set 256. The second step utilizes significantly lighter or darker than most of the image, the the estimated threshold IJ to split H into two subsets (H0 and contrast in those regions will not be sufficiently enhanced. H1), which are defined as, Adaptive histogram equalization (AHE) improves on this by transforming each pixel with a transformation function derived from neighbourhood region. It was first developed for use in aircraft cockpit displays[1]. HSV: HSL and HSV are the two most common cylindrical- coordinate representations of points in an RGB color model. The two representations rearrange the geometry of RGB in an attempt to be more intuitive and perceptually relevant than the Cartesian (cube) representation. Developed in the 1970s for computer graphics applications, HSL and HSV are used today in color pickers, in image editing software, and less commonly in image analysis and computer vision. HSL stands for hue, saturation and lightness and is also often called HLS. HSV stands for hue, saturation and value and is also often called HSB (B for brightness). A third model, common in computer vision applications, is HSI (I for intensity). However, while below.) In each cylinder, the angle around the central vertical axis corresponds to "hue", the distance from the axis corresponds to "saturation", and Fig 2. Proposed Method. the distance along the axis corresponds to "lightness", International Journal of Scientific Engineering and Technology Research Volume.06, IssueNo.24, July-2017, Pages: 4614-4619 De-noising and Contrast Enhancement using Bilateral Filter and Adaptive Histogram Equalization In its simplest form, each pixel is transformed based on the by scientists as a concept which is "to an extent applicable" histogram of a square surrounding the pixel, as in the figure in a situation, and it therefore implies gradations of below. The derivation of the transformation functions from significance. The best known example of a fuzzy concept the histograms is exactly the same as for ordinary histogram around the world is an amber traffic light, and indeed fuzzy equalization: The transformation function is proportional to concepts are widely used in traffic control systems[3]. the cumulative distribution function (CDF) of pixel values in Nowadays engineers, statisticians and programmers often the neighbourhood. Pixels near the image boundary have to represent fuzzy concepts mathematically using fuzzy be treated specially, because their neighbourhood would not variables, fuzzy sets and fuzzy values [4]. Since the 1970s, lie completely within the image. This applies for example to the use of fuzzy concepts has risen gigantically in all walks the pixels to the left or above the blue pixel in the figure. of life Fuzzy logic is a form of many-valued logic in which This can be solved by extending the image by mirroring the truth values of variables may be any real number pixel lines and columns with respect to the image boundary.
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