TYBSC (CS) SEM 6- DIGITAL IMAGE PROCESSING

Unit 2
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Image Enhancement
Q.14)

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Q.5 WRITE A NOTE ON IMAGE ENHANCEMENT USING SPATIAL FILTERS.
Spatial Filtering involves passing a weighted mask or kernel over the image and replacing the original
pixel values in the region corresponding to the kernel multiplied by kernel weights. (spatial filtering
and neighbourhood processing is same)

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III. Logarithmic Transformation
Logarithmic transformation further contains two type of transformation.
Log transformation and inverse log transformation. o or o The log transformations can be defined
by this formula o s = c log(r + 1).
Where s and r are the pixel values of the output and the input image and c is a constant.
The value 1 is added to each of the pixel value of the input image questions marked in red: QB
questions because if there is a pixel intensity of 0 in the image, then log (0) is equal to infinity.
So 1 is added, to make the minimum value at least 1. o During log transformation, the dark pixels in
an image are expanded as compare to the higher pixel values.
The higher pixel values are kind of compressed in log transformation

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1. EXPLAIN THE TERM
(A) THRESHOLDING (B) LOG TRANSFORMATION (C) NEGATIVE TRANSFORMATION (D) CONTRAST
STRETCHING (E) GREY LEVEL SLICING.
(A) Thresholding – covered above in non-linear transformations
(B) Log Transformation - covered above in non-linear transformations
(C) Negative Transformation – covered above in linear transformations
(D) Contrast Stretching Contrast stretching (often called normalization) is a simple image
enhancement technique that attempts to improve the contrast in an image by `stretching' the range
of intensity values it contains to span a desired range of values, e.g. the the full range of pixel values
that the image type concerned allows. It differs from the more sophisticated histogram equalization
in that it can only apply a linear scaling function to the image pixel values. As a result the
`enhancement' is less harsh. (Most implementations accept a graylevel image as input and produce
another graylevel image as output.)
(E) Grey Level Slicing - covered above in non-linear transformations
Q.22
Q.2) EXPLAIN THE TERMS: (A)SMOOTHING (B) SHARPENING
(A) Smoothing/Low pass filtering: Low pass filtering as the name suggests removes the high
frequency content from the image. It is used to remove noise present in the image. Noise, is

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normally a high frequency signal and low pass filtering eliminates the noise. Smoothing is used to
remove noise from image
(B) Sharpening/High pass filtering: o Sharpening is used for highlighting fine details in an image.
• Low Pass Filters: 1. Mean Filter/Averaging Filter/Low Pass Filter
Weighted Average Filter
Q.7
WRITE A NOTE ON WEIGHTED AVERAGE FILTERS. GIVE EXAMPLE.
This mask yields a so-called weighted average, terminology used to indicate that pixels are multiplied
by different coefficients, thus giving more importance (weight) to some pixels at the expense of
others. In the mask the pixel at the center of the mask is multiplied by a higher value than any other,
thus giving this pixel more importance in the calculation of the average.
Weighted Filter mask is as follows:

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17.EXPLAIN BIT PLANE SLICING WITH SUITABLE EXAMPLE.
19.JUSTIFY: ”BUTTERWORTH LOW PASS FILTER IS PREFERRED TO IDEAL LOW PASS FILTER
The ringing effects due to the sharp cut-offs in the ideal filter and to get rid of ringing effects,
elimination of sharp cut-offs is necessary. This exactly happens in butterworth low pass filters. The
transfer function of the butterworth low pass filter of order n and the cut off frequency at a distance
D0 from the origin is defined as

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8. WHAT ARE HIGH BOOST FILTERS? HOW ARE THEY USED? EXPLAIN. or 15.WHAT ARE
SHARPENING FILTERS? GIVE EXAMPLES. EXPLAIN ANY ONE IN DETAIL
Types of High Pass Filters:
1. High-boost Filtering

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2. Unsharp Masking
16.EXPLAIN VARIOUS TECHNIQUES OF IMAGE ARITHMETIC.
32. LIST AND EXPLAIN FIVE ARITHMETIC OPERATIONS ALONG WITH THEIR MATHEMATICAL
REPRESENTATION.
list:
1. Image Addition
2. Image Subtraction
3. Image Multiplication
4. Image Division
5. Alpha Blending

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33.EXPLAIN HOMOMORPHIC FILTER ALONG WITH BLOCK DIAGRAM OF HOMOMORPHIC FILTERING

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4. WHAT IS STRUCTURING ELEMENT? WHAT IS THE USE OF IT IN MORPHOLOGICAL
OPERATION?
1. Morphological techniques probe the image with a small shape or template called a
structuring element.
2. Structuring element is positioned at all possible locations in the image and its compared with
the corresponding neighbourhood of pixels.
3. A morphological operation on a binary image creates a new binary image in which the pixel
has a non-zero value only if the test is successful at that location in an input image.
4. The structuring element is a small binary image i.e a small matrix of pixels, each with a value
of zero or one.
5. The matrix dimensions specify the size of the structuring element
6. The patterns of ones and zeroes specifies the shape of the structuring element.
7. An origin of the structuring element is usually one of its pixels.
11.EXPLAIN THE MORPHOLOGICAL IMAGE OPERATIONS ON AN IMAGE. STATE ITS
AAPLICATIONS

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3. EXPLAIN DILATION AND EROSION AND EXPLAIN HOW OPENING AND CLOSING ARE
RELATED WITH THEM.
The basic morphological operations are dilation and erosion. They are expressed by a kernel
operating on an input binary image, X, where white pixels denote uniform regions and black
pixels denote region boundaries.
Erosion and dilation work conceptually by translating a structuring element, B, over the image
points and examining the difference between the translated kernel coordinates and image
coordinates.
Dilation and Erosion Based operations:
9. Applications of dilation and erosion:
Morphological operations are useful in many applications. To list a few they are used in hole
filling, boundary extraction of objects, extraction of connected components, Thinning and
thickening and so on. Among these applications the boundary extraction is shown below. For
comparison it is done with Sobel edge extraction
10.EXPLAIN EUCLIDEAN DISTANCE, CITY BLOCK DISTANCE, CHESS BOARD DISTANCE.

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18.DISCUSS VARIOUS COLOUR MODELS USED IN IMAGE PROCESSING.
37. LIST ANY FIVE COLOR MODELS AND EXPLAIN ANY TWO IN DETAILS.
21.EXPLAIN RGB COLOUR MODEL TO REPRESENT A DIGITAL IMAGE.

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36. LIST THE LIMITATIONS OF THE RGB COLOR MODEL.

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38. WRITE A SHORT NOTE ON HSI COLOR MODEL.

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34. EXPLAIN TWO TYPES OF CLASSIFICATION OF COLOR-QUANTISATION TECHNIQUES.
35.GIVE THE STEPS OF COLOR IMAGE QUANTISATION.

TYBSC (CS) SEM 6- DIGITAL IMAGE PROCESSING

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