Image Enhancement

CONTENT Overview Gray Scale Modification Image Sharpening Mapping Equations Histogram Modification Image Sharpening Image Smoothing

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. 2-2 Gray-level Stretching with Clipping at Both Ends Figure 8.2-2 Gray-level Stretching with Clipping at Both Ends. a) The mapping equation, b) the original image, c) the modified image with the stretch gray levels E[I(r,c)] Modified Gray Level Values 80 180 255 255 I(r,c) – Original Gray Level Values b a c

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. 2-10 Histogram Stretching with Clipping Figure 8.2-10 Histogram Stretching with Clipping. a) Original image, b) histogram of original image, c) image after histogram stretching with out clipping, d) histogram of image (c), e) image after histogram stretching with clipping 1% of the values at the high and low ends a b e c f d

Figure 8.2-11 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 (c) a b c d

Figure 8. 2-12 Histogram Slide Figure 8.2-12 Histogram Slide. The original image for these operations is the image from 8.2-11c 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 (c). a b d c

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 Rapidly changing brightness values – high freq Slowly changing brightness values – low freq Constant brightness – zero frequency 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 NxN-1, 5x5-1=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 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 Moore-Penrose 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