Image colouring code, Summaries of Digital Image Processing

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Breaking Bad

Spatial Domain Point Operation In Image enhancement

Basic Intensity

Transformation

Functions

Linear Functions:

Negative Transformation

Identity Transformation

Logarithmic Functions:

Log Transformation

Inverse-log Transformation

Power-Law Functions:

nth Power Transformation

nth Root Transformation

Advantages of Negative Transformation

Produces an equivalent of a

photographic negative.

Enhances white or gray detail

embedded in dark regions

Image R Image S

Log transformations function

The general form of the log transformation:

s = c log (1+r)

Where c is a constant, and r ≥ 0



Log curve maps a narrow range of low gray-level values in the input image into

a wider range of the output levels.



Used to expand the values of dark pixels in an image while compressing

the higher-level values.



It compresses the dynamic range of images with large variations in pixel

values.



Log functions are particularly useful when the input grey level values may

have an extremely large range of values



Logarithmic transformation are implemented using the expression:



g = c * log (1 + double (f))

Power-law transformation



Power-law(Gamma) transformations have the basic

form of:

s = c.r

ᵞ

Where c and y are positive constants



Map a narrow range of dark input values into a wider

range of output values or vice versa



Different transformation curves are obtained by varying

ᵞ (gamma)



If gamma <1 :the mapping is weighted toward brighter

output values.



If gamma =1 (default):the mapping is linear.

Sachin Paul

Power law

example

• The images to the right

show a magnetic resonance

(MR) image of a fractured

human spine

• Different curves highlight

different detail

Sachin Paul

mathematics of contrast

stretching

Contrast stretching works by linearly mapping the original intensity range to

a new range. This is achieved

Using the formula:

new intensity = (old intensity – min intensity)(new max – new min*

)/(max intensity – min intensity) +new mean

In this formula , old intensity is the original intensity value, min intensity

and max intensity are the minimum

And maximum intensity values in the original image, and new min and new

max are the desired minimum and maximum intensity values in the

stretched image. By applying this formula to every pixel in the image, the

contrast is effectively enhanced

BIT PLANE SLICING

Bit plane slicing is a technique used in image processing to

analyze the bit planes of an image. It involves separating the

binary representation of each pixel into its individual bits, and

then grouping those bits together to form new images that

highlight specific features of the original image.

This technique is particularly useful when dealing with images

that have complex patterns or textures, as it can reveal hidden

details that are not visible to the naked eye. It is also used in

data compression, where the most significant bits are retained

while the least significant bits are discarded.

APPLICATION OF CONTRAST

STRECTING

Contrast stretching is used in a variety of applications,

including medical imaging, satellite imagery, and digital

photography. In medical imaging, it can be used to enhance

the visibility of subtle features in X-ray or MRI images. In

satellite imagery, it can be used to improve the detection of

objects on the ground. In digital photography, it can be used

to correct for poor lighting conditions or camera settings.

In addition to enhancing contrast, contrast stretching can

also be used to normalize the intensity values of an image.

This is particularly useful when comparing images taken

under different lighting conditions or with different cameras

PPLICATION OF Bit plane slicing

Bit plane slicing is used in a variety of applications, including

image compression, pattern recognition, and computer

vision. In image compression, it can be used to reduce the

amount of data required to represent an image, by discarding

the least significant bits. In pattern recognition, it can be used

to extract features from an image that are relevant to a

particular task, such as facial recognition. In computer vision,

it can be used to segment an image into regions based on

texture or other visual features.

Bit plane slicing can also be used to detect errors in digital

images. For example, if a single bit error occurs during

transmission or storage, it may only affect one of the bit

planes, making it easier to detect and correct.