image enhancement techniques full report
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06042010, 06:15 PM
IMAGE ENHANCEMENT TECHNIQUES.ppt (Size: 161.5 KB / Downloads: 555) IMAGE ENHANCEMENT TECHNIQUES SUBMITTED BY INTRODUCTION Image enhancement widely used in computer graphics. It is the sub areas of image processing. The principle objectives of image enhancement techniques is to process an image so that the result is more suitable than the original image for a specific application . METHODS FOR IMAGE ENHANCEMENT Image enhancement techniques can be divided into two broad categories: 1.Spatial domain methods . 2 Frequency domain methods. SPATIAL DOMAIN METHODS The term spatial domain refers to the aggregate of pixels composing an image. Spatial domain methods are procedures that operate directly on these pixels. Spatial Domain processes will be denoted by the expression , g(x,y)= T[f(x,y)] POINT PROCESSING It is the process of contrast enhancement. It is the process to produced an image of higher contrast than the original by darkening a particular level. Enhancement at any point in an image depends only on the gray level at that point techniques in this category ore often referred to as point processing. Ã‚Â Median and Max/Min filtering Median filtering is a powerful smoothing technique that does not blur the edges significantly . Max/min filtering is used where the max or min value of the neighbourhood gray levels replaces the candidate pel . Shrinking and expansion are useful operations especially in two tone images. IMAGE SUBTRACTION The difference between two images f(x,y) and h(x,y) are expressed as, G(x,y)= f(x,y) â€œ h(x,y) Is obtained by computing the difference between all pairs of corresponding pixels from f and h. The key usefulness of subtraction is the enhancement of difference between images. One of the most commercially successful and beneficial uses of image subtraction is in the area of medical imaging called mask mode radiography . HISTOGRAM EQUALIZATION Histogram equalization is one of the most important parts for any image processing . This technique can be used on a whole image or just on a part of an image. Histogram equalization can be used to improve the visual appearance of an image. FREQUENCY DOMAIN METHODS We compute the Fourier transform of the image to be enhanced, multiply the result by a filter (rather than convolve in the spatial domain), and take the inverse transform to produce the enhanced image. IMAGE SMOOTHING The aim of image smoothing is to diminish the effects of camera noise, spurious pixel values, missing pixel values etc. Two methods used for image smoothing. neighborhood averaging and edge preserving smoothing. Neighbourhood Averaging Each point in the smoothed image,F(X,Y) is obtained from the average pixel value in a neighbourhood of (x,y) in the input image. For example, if we use a 3*3 neighbourhood around each pixel we would use the mask .Each pixel value is multiplied by 1/9, summed, and then the result placed in the output image Edge preserving smoothing An alternative approach is to use median filtering instead of neighborhood averaging. Here we set the grey level to be the median of the pixel values in the neighborhood of that pixel. The outcome of median filtering is that pixels with outlying values are forced to become more like their neighbors, but at the same time edges are preserved ,so this also known as edge preserving smoothing. Image sharpening The main aim in image sharpening is to highlight fine detail in the image, or to enhance detail that has been blurred Conclusion The aim of image enhancement is to improve the information in images for human viewers, or to provide `better' input for other automated image processing techniques There is no general theory for determining what is `good' image enhancement when it comes to human perception. If it looks good, it is good! THANK YOU Use Search at http://topicideas.net/search.php wisely To Get Information About Project Topic and Seminar ideas with report/source code along pdf and ppt presenaion



project report helper Active In SP Posts: 2,270 Joined: Sep 2010 
30092010, 01:36 PM
4Image Enhancement.ppt (Size: 1.26 MB / Downloads: 181) Image Enhancement Overview Human perception (focus of this discussion) Machine perception (ocr) Application specific Heuristic based: result better than the original image – subjective assessment Spatial vs frequency domain 


smart paper boy Active In SP Posts: 2,053 Joined: Jun 2011 
16072011, 04:32 PM
imgage enhancement seminar report.doc (Size: 130 KB / Downloads: 148) 1. An Introduction to Image Enhancement Image enhancement improves the quality (clarity) of images for human viewing. Removing blurring and noise, increasing contrast, and revealing details are examples of enhancement operations. For example, an image might be taken of an endothelial cell, which might be of low contrast and somewhat blurred. Reducing the noise and blurring and increasing the contrast range could enhance the image. The original image might have areas of very high and very low intensity, which mask details. An adaptive enhancement algorithm reveals these details. Adaptive algorithms adjust their operation based on the image information (pixels) being processed. In this case the mean intensity, Contrast and sharpness (amount of blur removal) could be adjusted based on the pixel intensity statistics in various areas of the image. 1.1 Defination of Image Enhancement: The principle objective of enhancement is to process an image so that the result is more suitable than the original image for a specific application. Image enhancement is one of the most interesting and visually appealing areas of image processing. 1.2 Image enhancement approaches fall into two broad categories: 1.2.1 Spatial domain and intensity transformations 1.2.2 Frequency domain approaches 1.2.1 Spatial domain: • Image plane • Image processing methods based on direct manipulation of pixels • Two principal image processing technique classifications 1. Intensity transformation methods 2. Spatial filtering methods Background 1.2.1a. spatial domain 1. Aggregate pixels composing an image 2. Computationally more efficient and require less processing resources for implementation 1.2.1b spatial domain processes denoted by the expression 1. g(x, y) = T[f(x, y)] 2. f(x, y) is input image 3. g(x, y) is output image 4. T is an operator on f, defined over some neighborhood of f(x, y) 5. T may also operate on a set of images (adding two images) 1.2.1c Neighborhood of a point (x, y) 1. Square or rectangular sub image area centered at (x, y) 2. Typically, the neighborhood is much smaller than the image 3. Center moves over each pixel in the image 4. T is applied at each point to get g at that location 5. Compute the average intensity of the neighborhood 6. Also possible to have neighborhood approximations in the form of a circle 7. The above application is also called spatial filtering 8. Neighborhood may be extracted by a spatial mask, or kernel, or template, or window. 1.2.1d Single pixel neighborhood or point processing techniques 1. Simplest form of T 2. Smallest possible neighborhood of size 1 × 1 g depends only on the value of f at a single point (r, c) 3. Graylevel transformation function of the form s = T® 4. r and s denote the gray level of f(x, y) and g(x, y) 5. Thresholding 6. Contrast stretching 1.2.1e larger pixel neighborhoods (mask processing or filtering) 1. a neighborhood around (x, y) to determine the value of g(x, y) 2. Neighborhood defined by masks, filters, kernels, templates, or windows (all refer to the same thing) 3. A kernel is a small 2D array whose coefficients determine the nature of the process 


priyadevachan Active In SP Posts: 1 Joined: Jul 2011 
19072011, 10:01 PM
sir
i want full report on image enhancement. 


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20072011, 10:08 AM
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smart paper boy Active In SP Posts: 2,053 Joined: Jun 2011 
21072011, 11:20 AM
enhancement.ppt (Size: 1.85 MB / Downloads: 90) Image Enhancement Overview Are employed to emphasize, sharpen & smooth image features for display and analysis Image enhancement is the process of applying these techniques to facilitate the development of a solution to a computer imaging problem Operate in the spatial domain, manipulating the pixel data, or in frequency domain, by modifying the spectral components (figure 8.1.2) Some used both Type of techniques : Point operations – where each pixel is modified according to a particular equation that is not dependent on other pixel values Mask operations  where each pixel is modified according to the values in a small neighborhood Global operations – where all the pixel values in the image are taken into consideration Spatial domain processing include all three but freq domain use global operations Gray Scale Modification Also called gray level scaling or gray level transformation, is the process of taking the original gray level values and changing them to improve image Relates to improving image contrast and brightness Image contrast is a measure of the distribution and range of gray levels – the difference between the brightest & darkest pixel values and how the intermediate values are arranged Image brightness refers to the overall average, or mean, pixel value in the image Mapping Equations One method to modify gray levels is a mapping equation mapping equation changes the pixel’s (gray level) values based on a mathematical function that uses brightness values as input The outputs of the equation are the enhanced pixel values Mapping is in the category of point operations Primary operations applied to gray scale image are to compress or stretch it Compress that are of little interest to us, and stretch where desire more information When the slope of the line is 0 – 1, gray level compression If the slope is greater than 1, gray level stretching See example 8.2.1 Figure 8.22 Graylevel Stretching with Clipping at Both Ends. a) The mapping equation, b) the original image, c) the modified image with the stretch gray levels Histogram Modification Use similar function which referred to as histogram modification Focus on histogram shape and range Histogram with a small spread has low contrast, histogram with a wide spread has high contrast Histogram clustered at the high end corresponds to a bright image Examination of histogram is useful tools, as it contains information of gray level distribution that easy to see the modifications that may improve Figure 8.210 Histogram Stretching with Clipping. a) Original image, b) histogram of original image, c) image after histogram stretching with out clipping, d) histogram of image ©, e) image after histogram stretching with clipping 1% of the values at the high and low ends Figure 8.211 Histogram Shrinking a) Original image, b) histogram of image (a), c) image after shrinking the histogram to the range [75,175], d) histogram of image © Figure 8.212 Histogram Slide. The original image for these operations is the image from 8.211c that had undergone a histogram shrink process. a) the resultant image from sliding the histogram down up 50, b) the histogram of image (a) , c) the resultant image from sliding the histogram down by 50, b) the histogram of image ©. Histogram equalization is an effective techn for improving the appearance of a poor image The function is the same as histogram stretch but often provides more visually pleasing results across a wider range of images Involves probability theory which treat as the probability distribution of gray levels Histogram equalization process consists 4 steps: Find the running sum of the histogram values Normalize the values from step (1) by dividing by the total number of pixels Multiply the values from step (2) by the maximum gray level value and round Map the gray level values to the results See example 8.2.6 IMAGE SHARPENING Image sharpening deals with enhancing detail information in an image, typically edges and textures Detail information is typically in the high spatial frequency information, so these methods include some form of highpass filtering Many image sharpening algorithms consist 3: Extract high frequency information Combine the high frequency image with original image to emphasize image detail maximizing image contrast via histogram manipulation High Frequency Emphasis Using a high boost spatial filter This mask is convolved with the image & value x determines the amount of low frequency information retained in the resulting image Value 8 – highpass filter (output image will contain only the edges) Larger values will retain more of ori image Less than 8 – negative of ori High Boost Spatial Filtering a) Original image b) results of performing a highboost spatial filter with a 3x3 mask and x = 6 c) histogram stretched version of (b) , note the image is a negative of the original, d) results of performing a highboost spatial filter with a 3x3 mask and x = 8 e) histogram stretched version of (d), note the image contains edge information only , f) results of performing a highboost spatial filter with a 3x3 mask and x = 12 g) histogram stretched version of (f) high boost mask can be extended with 1’s and a corresponding increase in the value x Larger masks will emphasize the edges more (make them wider), and help to mitigate the effects of any noise in original image If we create NxN mask, value x is NxN1, 5x51=24 Directional Difference Filters Similar to high boost filter but emphasize the edges in a specific direction This filters also called emboss filters, due to the effect they create on the output image Directional Difference Filters. a) Original image, b) image sharpened by adding the difference filter result to the original image, followed by a histogram stretch, c) 3x3 filter result with the +1 and 1 in the horizontal direction which emphasizes vertical lines, d) 3x3 filter result with the +1 and 1 in the vertical direction which emphasizes horizontal lines, e) 7x7 filter result with the +1 and 1 in the horizontal direction which emphasizes vertical lines, d) 7x7 filter result with the +1 and 1 in the vertical direction which emphasizes horizontal lines Homomorphic Filtering Digital images are created from optical images Optical images consist of 3 primary components, lighting & reflectance component Lighting component results from lighting conditions present when image is captured, & can change as the lighting conditions change reflectance component results from the way objects in the image reflect light & are determined by properties of object Many applications it is useful to enhance reflectance component, while reducing the contribution from the lighting component Homomorphic filtering is a freq domain filtering process that compresses the brightness (from the lighting conditions), while enhancing the contrast (from the reflectance) Image model is as follows: I(r,c) = L(r,c) R(r,c) where L(r,c) represents the contribution of lighting conditions, & R(r,c) represents the contribution of reflectance properties of objects Assumes that L(r,c) consists of primarily slow spatial changes (low spatial frequencies), & is responsible for overall range of brightness Assumptions for R(r,c), consists primarily of high spatial frequency information Consists of 5 steps: A natural log transform (base e) the Fourier transform Filtering the inverse Fourier transform, and the inverse log function – the exponential Decouple the L(r,c) & R(r,c) components Puts the image into freq domain Perform filtering Inverse transforms (step 2) Inverse step 1 Unsharp Masking Used by photographers to enhance image It sharpens image by subtracting a blurred (lowpass) version of original image This was accomplished during film development by superimposing a blurred negative onto corresponding film to produce a sharper result The process is similar to adding a detail enhanced (highpass) version to original To improve image contrast, include histogram modification as part of unsharp masking enhancement algorithm Original image is lowpass filtered, followed by histogram shrink Resultant image is subtracted from original image Histogram stretch to restore image contrast Different ranges of histogram shrinking Image Smoothing Used to give image softer or special effect, or to mitigate noise effects For spatial domain is by considering a pixel and its neighbors and eliminating any extreme values with median or mean filters In freq domain, is accomplished by some form of lowpass filtering Equivalent convolution mask can be approximated with MoorePenrose Some form of average (mean) filters The coefficients are all positive, unlike highpass filters Some common spatial convolution masks, where first 2 are standard arithmetic mean filters & last 2 are approximations to Gaussian filters 


sachin Damodhar Active In SP Posts: 1 Joined: Apr 2012 
02042012, 04:58 PM
i wants detail project and implimentation report on image enhancement with poor lighting by morphological block analysis.



