Digital Image Processing Image Enhancement in Spatial Domain Dr. Abdul Basit Siddiqui

Image Sampling and Quantization To create a digital image, we need to convert the continuous sensed data into digital form --> this involves sampling and quantization Digitizing the coordinate values is called sampling Digitizing the amplitude values is called quantization ---> see Fig. 2.16 in the textbook In practice, the method of sampling is determined by the sensor arrangement used to generate the image The quality of a digital image is determined by the number of samples and the number of gray levels 4/27/2017

Spatial resolution and intensity resolution Sampling is the principal factor defining the spatial resolution of an image, and quantization is the principal factor defining the intensity resolution Spatial resolution - number of rows and columns for example: 128 x128, 256 by 256, etc.; -->see Figs. 2.19 and 2.20 Intensity resolution - number of gray levels for example: 8 bits, 16 bits, etc.; --->see Fig. 2.21 4/27/2017

Image Enhancement 4/27/2017

Image Enhancement Process an image to make the result more suitable than the original image for a specific application –Image enhancement is subjective (problem /application oriented) Image enhancement methods: Spatial domain: Direct manipulation of pixel in an image (on the image plane) Frequency domain: Processing the image based on modifying the Fourier transform of an image Many techniques are based on various combinations of methods from these two categories 4/27/2017

Image Enhancement 4/27/2017

Basic Concepts Spatial domain enhancement methods can be generalized as g(x,y)=T[f(x,y)] f(x,y): input image g(x,y): processed (output) image T[*]: an operator on f (or a set of input images), defined over neighborhood of (x,y) Neighborhood about (x,y): a square or rectangular sub-image area centered at (x,y) 4/27/2017

Point Processes Point processes are the simplest of basic image processing operations. A point operation takes a single input image into a single output image in such a way that each output pixel's gray level depends only upon the gray level of the corresponding input pixel. Thus, a point operation cannot modify the spatial relationships within an image. Point operations transform the gray scale of an image. 4/27/2017

Point Processes Linear and nonlinear point operations Examples: Contrast Stretching Image Negatives Intensity-level Slicing Bit-plane Slicing Other Intensity Transformations Histogram Equalization 4/27/2017

Basic Concepts 4/27/2017

Basic Concepts g(x,y) = T [f(x,y)] Pixel/point operation: Neighborhood of size 1x1: g depends only on f at (x,y) T: a gray-level/intensity transformation/mapping function Let r = f(x,y) s = g(x,y) r and s represent gray levels of f and g at (x,y) Then s = T(r) Local operations: g depends on the predefined number of neighbors of f at (x,y) Implemented by using mask processing or filtering Masks (filters, windows, kernels, templates) : a small (e.g. 3×3) 2-D array, in which the values of the coefficients determine the nature of the process 4/27/2017

Common Pixel Operations Image Negatives Log Transformations Power-Law Transformations 4/27/2017

Image Negatives Reverses the gray level order For L gray levels the transformation function is s =T(r) = (L - 1) - r 4/27/2017

Image Negatives 4/27/2017

s =T(r) = a.r (a is a constant) Image Scaling s =T(r) = a.r (a is a constant) 4/27/2017

Function of s = cLog(1+r) Log Transformations Function of s = cLog(1+r) 4/27/2017

Log Transformations Properties of log transformations Application: –For lower amplitudes of input image the range of gray levels is expanded –For higher amplitudes of input image the range of gray levels is compressed Application: This transformation is suitable for the case when the dynamic range of a processed image far exceeds the capability of the display device (e.g. display of the Fourier spectrum of an image) Also called “dynamic-range compression / expansion” 4/27/2017

Log Transformations 4/27/2017

Power-Law Transformation 4/27/2017

Power-Law Transformation For γ < 1: Expands values of dark pixels, compress values of brighter pixels For γ > 1: Compresses values of dark pixels, expand values of brighter pixels If γ=1 & c=1: Identity transformation (s = r) A variety of devices (image capture, printing, display) respond according to power law and need to be corrected Gamma (γ) correction The process used to correct the power-law response phenomena 4/27/2017

Power-Law Transformation 4/27/2017

Gamma Correction 4/27/2017

Histogram Gray level histogram of an image - a function showing for each gray level the number of pixels in the image that have that gray level; it is simply a bar graph of the pixel intensities 4/27/2017

Histogram Histogram gives us a convenient, easy-to-read representation of concentration of pixels versus intensity in an image Dynamic range - an range of intensity values that occur in an image Contrast stretching - if image has low-dynamic range; low-dynamic range can result from poor illumination, lack of dynamic range in imaging sensor, wrong setting of the sensor parameters, etc. Compression of dynamic range - if the dynamic range of the image far exceeds the capability of the display device 4/27/2017

Histogram calculation 4/27/2017

Examples of several types of image histograms: 4/27/2017

Histogram Equalization Images with poor intensity distributions can often be enhanced with histogram equalization <-- point process The goal is to obtain a uniform histogram • Histogram equalization will not flatten a histogram; if a histogram has peaks and valleys it will still have them after equalization - they will be shifted and spread over the entire range of image intensities Works best on images with fine details in darker regions Use it carefully - good images can be often degraded by histogram equalization 4/27/2017

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Histogram of the equalized image 4/27/2017

Histogram Equalization-Example 4/27/2017