Digital Image Enhancement Methods for Multimedia Technology

digital ambit sweetener Methods for Mul datedia TechnologyChapter 11.1 IntroductionIn todays communications networks, multimedia is a growing field. There ar increasing demands on incorporating visual brass to other modes of communications. It is therefore unable to be eliminateed to eat up situations in which the word catch of speech and transmitted go steadys being crooked or truehearted in their perceptual grapheme by variety of ways.1.2Digital doubling ProcessingAn hear is delineate as two- dimensional function, f(x,y), where x,y argon plane coordinates and the amplitude of f at whatever pair of coordinates (x,y) is c every last(predicate)ed the intensity or gray take aim of the cooking stove. When x, y and the intensity observes of f ar entirely told finite and separate quantities, we call the image a digital image. To processing the image by remembers of computer algorithmic programic programic programs is called as digital image processing. As comp ar d to running(a) image processing, digital image processing has many advantages. It flowerpot avoid problems such as signal distortion, image degradation and build-up of intervention during processing.1.2 Image Resto proportionalityn and Enhancement MethodsNow days digital images have covered the complete world. Images atomic offspring 18 acquired by photo electronic or photochemical methods. The sensing devices tend to reduce a quality of the digital images by introducing the echo and blur due to motion or misfocus of camera. single of the commitoff applications of digital images was in the in the altogethers paper industry, when pictures were sent by submarine cable between New York and London. Introduction of cable picture transmission body in the early 1920s decreased the time required to transport a picture across Atlantic from more than a week to less than three hours. rough of the initial problems in improving the visual quality of these early digital pictures were cogitate to the selection of printing procedures and scattering of intensity levels.Digital image processing techniques began in the late 1960s and early 1970s to be use in medical imaging, remote Earth resources comments and astronomy.Tomography was invented independently by Sir Godfrey N. Hounsfield and professor Allan M.Cormack who sh atomic reduce 18d the 1979 Nobel Prize in medicine for their invention. But, roentgen rays were discovered in 1985 by Wilhelm Conrad Roentgen. Geographers use the similar technique to study the pollution patterns from aerial and transmit imagery. Image sweetening and restoration procedures argon used to process the degraded images of unrecoverable objects or experimental results too expensive to duplicate. The use of a gray level multi furtheriousnessation which transforms a given empirical distribution function of gray level assesss in an image into a uniform distribution has been used as an image enhancement as well as for a normaliz ation procedure.( I. Pitas)Image enhancement refers to sum up the image quality by sharpening certain image features ( leapings, boundaries and contrast) and reducing the hoo-hah. Digital image enhancement and restoration are two dimensional trys. They are resistantly classified into unidimensional digital tenses and non linear pick ups. Linear digital filter hind end be designed or implemented any spatial worldly concern or Frequency domain. (K.S. Thyagarajan)In Spatial study methods refers to the image plane itself .Image processing methods, spatial domain methods are establish on direct manipulation of pels in an image. The intensity transformations and spatial filtering are two principal categories of spatial domain methods.In Frequency domain methods, first image is transformed to relative frequency domain. It dream ups that, the Fourier transform of the image is computed and performed all processing on the Fourier transform of the image. Finally Inverse Fourier tr ansform is performed to get the resultant image. (Rafael C.Gonzalez and Richard E.Woods)Image Enhancement Techniques are medial(prenominal) filtering resemblance averagingEdge DetectionHistogram techniquesIn 1980, recent work on c.c.d. scanners is reviewed and solid-state scanners which include on-chip signal processing functions are described. Future trends are towards heady scanners these are scanners with on-chip real-time processing functions, such as analogue-to-digital conversion, thresholding, info compaction, frame enhancement and other real-time image processing functions.( Chamberlain,1980)The image enhancement algorithm first separates an image into its offsets (low-pass filtered form) and highs (high-pass filtered form) characters. The lows comp unrivallednt thus controls the amplitude of the highs component to increase the topical anaesthetic contrast. The lows component is then issuinged to a non-linearity to modify the local luminance mean of the image and i s combined with the processed highs component. The performance of this algorithm when employ to enhance typical undegraded images, images with large shaded areas, and also images degraded by cloud cover will be illustrated by way of warnings. (Peli, T., 1981)Enhancement algorithms establish on local normals and interquartile distances are more setive than those employ means and standard deviations for the removal of spike noise, preserve edge moroseness better and introduce fewer artifacts around high contrast edges. They are not as fast as the mean-standard deviation equivalents but are competent for large data sets treated in small machines in production quantities.( Scollar,I.,1983) reaching CT images to abrogate noise, and thereby enhance the signal-to-noise ratio in the images, is a difficult process because CT noise is of a broad-band spatial-frequency character, overlapping frequencies of interest in the signal.A measurement of the noise power spectrum of a CT scanner and some form of spatially variant filtering of CT images can be beneficial if the filtering process is based upon the residuals between the frequency characteristics of the noise and the signal. For evaluating the performance, used a percentage standard deviation, an index representing contrast, a frequency spectral pattern, and several CT images processed with the filter. (Okada., 1985)A matte least-mean-square (TDLMS) adjustive algorithm based on the method of steepest decent is proposed and applied to noise reduction in images. The adaptive property of the TDLMS algorithm enables the filter to have an improved tracking performance in nonstationary images. The results presented show that the TDLMS algorithm can be used successfully to reduce noise in images. The algorithm complexity is 2(NN) multiplications and the identical number of additions per image sample, where N is the parameter-matrix dimension. The algorithm can be used in a number of savourless applications such as image enhancement and image data processing.( Hadhoud,M.M.,1988)Image processing techniques are used to determine the commence and alignment of a land vehicle. The approach taken is to establish a state sender of quantities derived from an image range, and to refine this over the mission. The image processing techniques applied generate into the generic categories of enhancement, detection, segmentation, and classification. Approaches to estimating the alignment and range of a vehicle in computationally efficient ways are presented. The estimates of quantities extracted from single image frames are subject to errors. This approach facilitates the integration of results from multiple images, and from multiple sensor systems.( Atherton, T.J.,1990)The JPEG coder has proved to be extremely useful in coding image data. For low bit-rate image coding (0.75 bit or less per pixel), however, the block effect becomes very annoying. The edges also display wave-like appearance. An enhan cement algorithm is proposed to enhance the indwelling quality of the reconstructed images. First, the pixels of the coded image are classified into three broad categories (a) pixels belonging to quasi-constant regions where the pixel intensity quantifys vary slowly, (b) pixels belonging to dominant-edge (DE) regions which are characterized by few sharp and dominant edges and (c) pixels belonging to textured regions which are characterized by many small edges and thin-line signals. An adaptive mixture of some long-familiar spatial filters which uses the pixel research laboratoryeling information for its adaptation is used as the adaptive optimal spatial filter for image enhancement. (Kundu, A.1995)The videotexts are low- resolving power and heterogeneous with complex backgrounds image enhancement is a key to successful fruition of the videotexts. Especially in Hangul characters, several consonants cannot be distinguished without sophisticated image enhancement techniques. In thi s experiment, after multiple videotext frames containing the same provides are notice and the caption area in each frame is extracted, five variant image enhancement techniques are serially applied to the image multi-frame integration, resolution enhancement, contrast enhancement, advanced binarization, and morphological smoothing operations and tested the proposed techniques with the video caption images containing both Hangul and English characters from various video sources such as cinema, news, sports, etcetera The character recognition results are greatly improved by use enhanced images in the experiment. (Sangshin Kwak.,2000).The use of an adaptive image enhancement system that implements the human visual system (HVS) has the properties for contrast enhancement of X-ray images. X-ray images are poor quality and are normally interpreted visually. The HVS properties considered are its adaptive nature, multichannel mechanism and high nonlinearity. This method is adaptive, n onlinear and multichannel, and combines adaptive filters and homomorphic processing.The normal filtering method is a simple and efficient way to remove longing noise from digital images. This novel method has two gifts. The first ramification is to detect the heart rate noise in the image. In this stage, first one identify the noise pixel and second one the pixels are rough divided into two classes, which are noise-free pixel and noise pixel. Then, the second stage is to eliminate the impulse noise from the image. In this stage, and the noise-pixels are processed. The noise -free pixels are directly copied to the outfit image. Here, hybrid of adaptive clean(prenominal) filter with switching medial filter method is used. The adaptive normal filter framework in mark to enable the flexibility of the filter to change it coat accordingly based on the approximation of local noise density. The switching median filter framework in order to advance up the process and also al lows local details in the image to be preserved. (Kong, NSP., 2008)One of the advantages of Level-2 Improved tolerance based selective arithmetical mean filtering technique is that this filtering technique is to detect and remove the rackety pixels and regenerate the noise free information. However the removal of impulse noise is lots accomplished at the expense of blurred and distorted features of edges. Therefore it is needed to preserve the edges and fine details during filtering. (Deivalakshmi,S., 2010)An efficient non-linear cascade filter is used to removal of high density salt and peppercorn noise in image and video. This method consists of two stages to enhance the filtering. The first stage is the finis based Median Filter (DMF) which is used to identify pixels likely to be contaminated by salt and pepper noise and replaces them by the median esteem. The second stage is the Unsymmetrical Trimmed Filter, either Mean Filter (UTMF) or Midpoint Filter (UTMP) which is us ed to trim the noisy pixels in an unsymmetrical manner and processes with the remaining pixels The basic mind is that, though the level of denoising in the first stage is lesser at high noise densities, the second stage helps to increase the noise suppression. Hence, this method is very suitable for low, medium as well as high noise densities fifty-fifty to a higher place 90%. This algorithm shows better image and video quality in terms of visual appearance and quantitative measures. ( Balasubramanian, S.,2009)The enhancement algorithm enhances CR image detail and CR image enhanced has ethical visual effect, so the method id suit for edge detail enhancement of CR medicine radiation image. (Zhang., 2010).Three dimensional TV is considered as next generation broadcasting service.TOF sensors are a relatively new technology allowing real time capture of both photometric and nonrepresentational scene information. In order to generate the natural 3D video, first we develop a practica l pipeline including TOF data processing and MPEG-4 based data transmission and reception. Then we acquire colour and depth videos from TOF range sensor. Then Alpha matting and enhancement are performed to handle dazed and hairy objects (Ji-Ho Cho Sung-Yeol Kim Lee, 2010).Chapter 22.1 Median FilteringMedian Filtering is a non -linear signal enhancement technique for the smoothing of signals, the suppression of impulse noise, and preserving of edges. In the one dimensional causa it consists of sliding a windowpane of an odd number of elements along the signal, substitute the warmness sample by the median of the samples in the window.Noise is any undesirable signal. Noise is everywhere and thus we have to learn to have a go at it with it. Noise gets introduced into data via any electrical system used for storage, transmission, and/or processing. In addition, nature will always play a noisy trick or two with data under observation.When encountering an image corrupted with noise you will want to improve its appearance for a circumstantial application. The Techniques applied are application-oriented. Also, different procedures are related to the types of noise introduced to the image. or so important types of noise are Gaussian or vacuous, Rayleigh, Salt-pepper or impulse noise, periodic, sinusoidal or coherent, uncorrelated, and granular.In statistics, a median is described as the numeric protect separating the higher half of a sample, a population, or a probability distribution, from the lower half. The median of a finite bring up of numbers can be found by arranging all the numbers from lowest value to highest value and picking the centre one.For exampleThe observations are 7,5,6,8,1,3,8,5,4.First, we are arranging in ascending order or lowest value to highest value.1, 3, 4, 5, 5, 6, 7, 8, 8Then the middle one is picked. Here, number of observations n=9, it is an odd number.The middle value=5.So, the median =5.If there is an even number of observati ons, then there is no single middle value the median is then usually defined to be the mean of the two middle values.For example observations are 7,5,6,8,1,3,8,5,4,6.First, we are arranging in ascending order or lowest value to highest value.1, 3, 4, 5, 5, 6, 6, 7, 8, 8Then the middle one is picked. Here, number of observations n=10, it is an even number.So, averaging the observation 5 and 6 and gets the median value.The observation values are 5 and 6.The averaging value of 5 and 6 gives 5.5.So, the median =5.5.Most scanned images contain noise caused by the scanning method (sensor and its calibration-electrical components, communicate frequency spikes) this noise may look like dots of black and white.Median filter helps us by erasing the black dots, called the Pepper, and it also fills in white holes in an image, called salt Impulse Noise. Its like the mean filter but is better in pixels and will not consider the other pixels significantly. This means that mean does that.Preservi ng sharp edgesMedian value is more like neighbourhoodMedian filtering is popular in removing salt and pepper noise and works by replacing the pixel value with the median value in the neighbourhood of that pixel. When applied on1. We do lighter -ranking by first placing the brightness values of the pixels from each neighbourhood in ascending order.2. The median or middle value of this ordered sequence is then selected as the representative brightness value for that neighbourhood.2.2Median Filter exerciseThe median filter is also sliding -window spatial filter, but it replaces the centre pixel value in the window by the median of all pixel values in the window. As for the mean filter, the kernel is usually square but can be any shape rectangular, circular, etc depends on an image. An example of median filtering of a single 3*3 window of values is shown in figure 2.1.To arrange the pixel value in ascending order 0,2,3,3,4,6,19,97The median value=4(Here no of items=9)The centre pixel value 97 is replaced by the median value 4 as shown below.Figure 2.2This illustrates one of the celebrated features of the median filter its ability to remove impulse noise. The median filter is also widely claimed to be edge-preserving since it theoretically preserves step edges without blurring. However, in the presence of noise it blurs edges in images slightly.2.3 semi synthetical ImageLet us consider 6*6 window size of it.Here, we take 3*3 suppress size, to scrape up out the median value.The order of the pixel value1,2,3,3,3,4,5,7,8.The median value of this sham size=3.Here, the centre pixel value 3 is replaced by the median value 3.Here, we find out the A to P value as shown in figure 2.5. First, we find out the median value for 3*3 mask size and replacing the original centre pixel value by these values.To find AOrder 1, 2, 3,3,3,4,5,7,8.Median=3.To find BOrder 1, 3, 3,3,4,4,5,6,8.Median=4.To find COrder 2, 3, 3,4,4,5,6,8,9.Median=4.To find DOrder 1, 2, 2,3,4,5,6,8,9.Med ian=4.Similar way, we have to calculate F to P.To find POrder 2, 4,5,5,5,8,8,9Median=5.The final output of synthetic image of 6*6 window as shown in figure 2.6.By checking the synthetic image output by using Matlab. To refer the Matlab Coding in Appendix A.Output3 1 5 6 9 27 3 4 4 4 12 4 4 4 4 81 4 4 4 5 71 4 4 5 5 83 5 7 9 8 2Both ease up calculation synthetic image output and Matlab synthetic image output are same.2.4 Median Filter Implementation on Mat labIn past years, linear filters become the most popular filters in image processing. The reason of their popularity is caused by the existence of robust mathematical models which can be used for their analysis and design. However, there exist many areas in which the nonlinear filters provide significantly better results. The advantage of non linear filters lies in their ability to preserve edges and suppress the noise without loss of details. The success of nonlinear filters is caused by the fact that image signals as well as l iving noise types are usually nonlinear.Due to the imperfection of image sensors, images are often corrupted by noise. The impulse noise is the most a great deal referred type of noise. The most cases, impulse noise is caused by malfunctioning pixels in camera sensors, faulty memory locations in hardware, or errors in data transmission. We distinguish two common types of impulse noise. They are Salt-and-Pepper noise and the haphazard treasured shot noise. For images corrupted by salt-and-pepper noise, the noisy pixels have only maximum or minimum values. In case of random valued shot noise, the noisy pixels have arbitrary value.Traditionally, the impulse noise is upstage by a median filter which is the most popular non linear filter .A standard median filter gives poor performance for images corrupted by impulse noise with higher intensity. A simple median filter utilizing 3*3 or 5*5 pixel window is sufficient only when the noise intensity is less than around 10-20%.Here, we imp lement the median filter using Matlab. To refer the Matlab coding in Appendix B.OutputproblemThe Noisy Image is corrupted by Salt-and-Pepper noise. By using median filter, 3*3 mask size most of noise has been eliminated.If we smooth the noisy image with larger median filter 7*7 mask size, all the noisy pixels depart as shown above figure.3.0 Neighbourhood Averaging FiltersNeighborhood averaging filters are similar to mean filters. The Neighborhood averaging filter is the simplest low pass filter here all coefficients are identical. These filters sometimes are called Averaging filters. The characteristics of part averaging are defined by kernel height, width and shape. When Kernel size increases, the smoothing effect also increases. The idea behind these filters is straight forward. By replacing the every pixel value in an image by the average of the intensity levels in the neighborhood defined by the filter mask, this process results in an image with reduced sharp transitions in i ntensity levels. The window is usually square, but can be any shape like rectangular, circular, etc. depending on the size of an image.Each point in the smoothed image, is f(x,y)obtained from the average pixel value in a neighbourhood of (x,y) in the input image.For example, if we use a 33 neighbourhood around each pixel we would use the maskEach pixel value is multiplied by 1/9, summed, and then the result placed in the output image. This mask is successively travel across the image until every pixel has been covered. That is, the image is convolved with this smoothing mask (also cognise as a spatial filter or kernel).However, one usually expects the value of a pixel to be more closely related to the values of pixels close to it than to those further away. This is because most points in an image are spatially coherent with their neighbours indeed it is generally only at edge or feature points where this hypothesis is not valid. Accordingly it is usual to weight unit the pixels ne ar the centre of the mask more strongly than those at the edge.Some common weighting functions include the rectangular weighting function above (which just takes the average over the window), a triangular weighting function, or a Gaussian.In practice one doesnt notice much difference between different weighting functions, although Gaussian smoothing is the most commonly used. Gaussian smoothing has the attribute that the frequency components of the image are modified in a smooth manner.Smoothing reduces or attenuates the higher frequencies in the image. Mask shapes other than the Gaussian can do odd things to the frequency spectrum, but as far as the appearance of the image is concerned we usually dont notice much.The arithmetic mean is the standard average, often simply called the mean.The mean may be confused with the median, mode or range. The mean is the average of a set of values, or distribution however, for probability distributions, the mean is not necessarily the same as th e median, or the mode.For exampleThe observations are 7,5,6,8,1,3,8,5,4.First, we find out the organic value for these observations.Total=7+5+6+8+1+3+8+5+4=47Then, finding the average one. Here, number of observations n=9. average out=total/9.=47/9Average=5.22(Equivalent to 5)So, the average =5.3.1 Synthetic imageLet us consider 6*6 window size.Figure 3.1Here, we take 3*3 mask size, to find out the Neighbourhood averaging value.The order of the pixel value1,2,3,3,3,4,5,7,8.The averaging value of this mask size=4.Here , the centre pixel value 3 is replaced by the averaging value 4.By using this method, we have to calculate the median value for whole window size 6*6.3156927AB