Intensification Of Dark Mode Images Using FFT And Bilog Transformation

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1 Intensification Of Dark Mode Images Using FFT And Bilog Transformation Yeleshetty Dhruthi 1, Shilpa A 2, Sherine Mary R 3 Final year Students 1, 2, Assistant Professor 3 Department of CSE, Dhanalakshmi College of Engineering, Chennai, India Abstract: With the advancement of technologies, the images are created with more and more enhancement. Our project is based on creating a generalized equalization model for image enhancement and further improving the same using Fast Fourier Transform and Bilog Transformation. Here, we analyse the relationship between image histogram and contrast enhancement and white balancing. In the proposed system, we enhance not only images, but also videos both live and recorded. The original image is stored in RAW format which is too big for normal displays. Hence we make use of tone mapping techniques such as Gamma Correction. White Balancing and Contrast Enhancement are two categories of tone mapping. We make use of histogram analysis which is the combination of both the categories of tone mapping in this process. In order to enhance the generalized equalization model, FFT filter is used to find the intensity, i.e., whether the image has low resolution or high resolution. Then, in order to naturalize the image we use Bilog transformation. Using Bilog transformation, we map the illumination to make a balance between the details and naturalness. Then we use NTSC colour space evaluation in order to get the complete enhanced image. Keywords: Gamma correction, white balancing, FFT, Bilog transformation I. INTRODUCTION Image processing usually refers to digital image processing, but there areanalog and optical image processing too which are possible. Imaging is the term coined for the acquisition of images (producing the input image in the first place). Closely related to image processing are computer graphics and computer vision. In computer graphics, instead of the images being acquired (via imaging devices such as cameras) from natural scenes, images are manually made from physical models of objects, environments, and lighting, just like in most animated movies. On the other hand, Computer vision is often considered high-level image processing out of which a machine/computer/software intends to decipher the physical contents of an image or a sequence of images (e.g., videos or 3D full-body magnetic resonance scans). II. EXISTING METHOD A. Generalized Equalization Model: If f=(f r, f g, f b ) T is the image, then the available dynamic range of f c is [0,L c ], c= r, g, b. The histogram is denoted as {h c,p c } c = r, g, b. In the existing technique, we use the following: Histogram-Based Analysis technique on White Balancing Gamma Correction Image processing is the form of processinginputs which are of various kinds: such as an image, photograph or a video frame; the output of such a process may be either an image or a set of characteristics or parameters related to the image. Most image-processing techniques will involve treating the image like a two- dimensional signal and then applying standard signal-processing techniques to it. Page 186

2 rapidly computes such transformations by factorizing the DFT matrix into a product of sparse (mostly zero) factors. Input image R G B Tonal range Fig 1. Histogram Analysis of a particular picture White Balancing The image is expressed in the Lambertian surface model as follows: HSV conversion = (λ)l(λ) (λ) λ H S V Here, λ is the wavelength of visiblelight r( λ) is thesurfacereflectance, l( λ)is the light source,and m c ( λ) is thesensitivityofcamerainthechannel. Thegoalofcolorconstancyistoestimatetheproje ctionoflightsourceonthergbspace. Many assumptions have been madeto achieve this goal. The existing model (See Fig. 2 ) aims in giving a joint algorithm for both white balancing and contrast enhancement problem. Histogram analysis Weighting distribution Gamma Correction Color space Evaluation However, the naturalness of the image is not preserved as real as the object captured in the image. Hence we go into the proposed system which contains transformations to the image to preserve naturalness. III. 1. FFT PROPOSED METHODOLOGY Fast Fourier transform (FFT) is an algorithm to compute the discrete Fourier transform (DFT) and it s inverse. Fourier analysis converts time (or space) to frequency (or wavenumber) and vice versa; an FFT Enhanced output image Fig.2. Flowchart of Existing method using Generalized Equalization model As a result, fast Fourier transforms are widely used for many applications in engineering, science, and mathematics. The DFT is obtained by decomposing a sequence of values into components of different frequencies. Page 187

3 ISSN (Online) This operation is useful in many fields but computing it directly from the definition is often too slow to be practical. (, ) = 1 (, ) An FFT is a way to compute the same result more quickly: computing the DFT of N points in the naive way, using the definition, takes O(N 2 ) arithmetical operations, while an FFT on the other hand can compute the same DFT in only O(N log 2 N) operations. The difference in speed can be quite huge, especially for long data sets where N may be in the thousands or millions. In practice, the computation time can be reduced by several orders of magnitude in such cases, and the improvement is more or less proportional to N /log(n). This huge improvement made the calculation of the DFT practical and simple to use. For a square image of size N N, the two-dimensional DFT is given by: (, ) = (, ) where f(a,b) is considered to be the image in the spatial domain and the exponential term is the basis function with respect to each point in the Fourier space,f(k,l). The equation can be interpreted as: Each point F(k,l)has a value associated to it and it is obtained by multiplying the spatial image with the corresponding base function and then the result is summed up. Similarly, the Fourier image can be transformed back into the spatial domain by using Inverse Fourier Transform. It is given as follows: (, ) = 1 (, ) ( ) ( ) Where, (, ) = 1 (, ) Even with these computational savings, the ordinary one-dimensional DFT has N 2 complexity. This can be reduced to N log 2 N if we employ the Fast Fourier Transform (FFT) to compute the onedimensional DFTs. This is a remarkable improvement, in particular for larger images. There exist various forms of the FFT and most of them restrict the size of the input image that may be transformed, often to N=2 n where n is an integer. The Fourier Transform produces an output image in the form of a complex number value, which can be displayed with two images, either with the type (i) real and the imaginary part or with the type (ii) magnitude and phase. Only the Fourier Transform s magnitude is displayed in image processing often, as the magnitude contains most of the information of the geometric structure of the spatial domain image. However, we must make sure to preserve both magnitude and phase of the Fourier image, if we want to re-transform the Fourier image into the correct spatial domain after some processing is done in the frequency domain. 2. Bilog Transformation This is used to further filter out the negative components that are filtered in the FFT process. This is then again inversed to get the enhanced image. In order to obtain the result for the above equations, for each image point, a double sum must be calculated. However, because of the fact thatfourier Transform is separable, it can be written as follows: Page 188

4 Input image A. Apply Fast Fourier Transform and Shift Aero Frequency Components This RGB Color Panels have to be segregated to grayscale images i.e.,binary images. HSV. Red Panel Green Panel Blue Panel H = arcos{ 1 2 (2 ) ( ) ( )( ) } Fast Fourier Transform (FFT) Sift Zero Frequency Component = max(,, ) min (,, ) max (,, ) Inverse FFT Bilog Transform R G B Restoration Factor of RGB Panel = We take the value of V (intensity) for analysis of Histogram, Gamma Correction. For filtering the pixels based on their intensity, we use FFT filter. The frequency components that are low or zero frequency components are shifted to the centre of the array matrix in frequency domain. B. Inverse FFT In order to reconstruct the image in frequency domainto its spatial domain so as to picturize theimage, we perform inverse operation. C. Bilog Transformation NTSC Color Space Enhancement Enhanced Output Image Fig. 3. Flowchart of the proposed Algorithm using FFT and Bilog Transformation: The input image is split up into red, blue and green panels and then Fast Fourier Transform is applied. The Flowchart (in Fig. 3.) is explained block by block below: We can still find some traces of negative frequency components or zero frequency components. Bilog Transformation is used to perform action on such kind of low frequency information. Clustering process is done by which such pixels are grouped together to increase the resolution of the pixels. D. Restoration of RGB Color Panels: At this stage, the pixels are converted back to RGB Color model and they are highlighted to a certain extent. E. NTSC Color Space Enhancement: The abbreviation of National Television System Committee is NTSC. It is widely used in The United States. In this, RGB is converted to YIQ and then the YIQ panels are converted back to RGB Color model to Page 189

5 process the grayscale and color information present in the image. Besides images, the proposed system also includes enhancing videos both live and recorded. For videos, the frames have been split and the FFT and Bilog transformations are applied to each of the frames and the output Enhanced video is got. The proposed system has also got the feature of Enhancing Live videos. Bird image: Input Image: Performance Evaluation: Output Enhanced Image: Image Input image resolution Proposed method resolution Nightfall Bird image Medical Lung Image: M e d CONCLUSION Lowlit bedroom image: Thus, the system proposed has got the Enhanced version of image that is captured in the dark with more of the unnoticed features visible. We have used Fast Fourier Transform and Bilog transformation for images and motion pictures or videos (both live and recorded) the performance measures have been identified efficient. References: [1] Hongteng Xu, Guantao Zhai, Xiaolin Wu, Xiaokang Yang, Generalized Equalization Model for Image Enhancement IEEE Transactions on Multimedia, Vol. 16, No. 1, January 2014, Page 190

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