Image Processing

Aspects of Image Processing

• • Image Enhancement: Processing an image so that the result is more suitable for a particular application. (sharpening or deblurring an out of focus image, highlighting edges, improving image contrast, or brightening an image, removing noise) 

  • Image Restoration: This may be considered as reversing the damage done to an image by a known cause. (removing of blur caused by linear motion, removal of optical distortions) 

  • Image Segmentation: This involves subdividing an image into constituent parts, or isolating certain aspects of an image. (finding lines, circles, or particular shapes in an image, in an aerial photograph, identifying cars, trees, buildings, or roads 

Types of Digital Images

Binary: Each pixel is just black or white. Since there are only two possible values for each pixel (0,1), we only need one bit per pixel.

Grayscale: Each pixel is a shade of gray, normally from 0(black) to 255 (white). This range means that each pixel can be represented by eight bits, or exactly one byte. Other greyscale ranges are used, but generally they are a power of 2.

True Color: or RGB: Each pixel has a particular color; that color is described by the amount of red, green and blue in it. If each of these components has a range 0–255, this gives a total of 256 3 different possible colors. Such an image is a “stack” of three matrices; representing the red, green and blue values for each pixel. This means that for every pixel there correspond 3 values.

Bit Planes

Greyscale images can be transformed into a sequence of binary images by breaking them up into their bit-planes. We consider the grey value of each pixel of an 8-bit image as an 8-bit binary word. The 0th bit plane consists of the last bit of each grey value. Since this bit has the least effect (least significant bit plane).

The 7th bit plane consists of the first bit in each value most significant bit plane.

Aditions

Subtractions

Multiplications

Complement

Histograms

Given a grayscale image, its histogram consists of the histogram of its gray levels; that is, a graph indicating the number of times each gray level occurs in the image. We can infer a great deal about the appearance of an image from its histogram. In a dark image, the gray levels would be clustered at the lower end. In a uniformly bright image, the gray levels would be clustered at the upper end. In a well contrasted image, the gray levels would be well spread out over much of the range. Problem: Given a poorly contrasted image, we would like to enhance its contrast, by spreading out its histogram. There are two ways of doing this

Thresholding

Single thresholding: A grayscale image is turned into a binary image by first choosing a gray level T in the original image, and then turning every pixel black or white according to whether its gray value is greater than or less than T.

• • A pixel becomes white if its gray level is > T 

  • A pixel becomes black if its gray level is <= T 

Double thresholding: Here we choose two values T1 and T2 and apply a thresholding operation as:

• • A pixel becomes white if its gray level between T1 and T2 

  • A pixel becomes black if its gray level is otherwise

Frequencies; Low and High Pass Filters

Frequencies are the amount by which grey values change with distance.High frequency components are characterized by large changes in grey values over small distances; (edges and noise). Low frequency components are parts characterized by little change in the gray values. (backgrounds, skin textures).

• • High pass filter: if it “passes over” the high frequency components, and reduces or eliminates low frequency components. 

  • Low pass filter: if it “passes over” the low frequency components, and reduces or eliminates high frequency components. 

Noise

Noise is any degradation in the image signal, caused by external disturbance. Salt and pepper noise: It is caused by sharp, sudden disturbances in the image signal; it is randomly scattered white or black (or both) pixels. It can be modeled by random values added to an image

• • Gaussian noise: is an idealized form of white noise, which is caused by random fluctuations in the signal. 

  • Speckle noise: It is a major problem in some radar applications. It can be modeled by random values multiplied by pixel values. 

References

• http://www.igp.ethz.ch/photogrammetry/education/lehrveranstaltungen/photogrammetry/matlab_imageprocessing.pdf

Authors

• • Facundo Triay & Nadina Battagliotti. Both IEEE Members and researchers @ AIGROUP, working in the PI1 Project.