THEORY EXAMINATION (SEM–VIII) 2016-17 DIGITAL IMAGE PROCESSING

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SECTION – A

Attempt all parts (Short Answer Explanation)

A digital image is a two-dimensional function that represents visual information in the form of pixels. Dynamic range refers to the difference between the minimum and maximum intensity values present in an image. It determines how bright or dark an image can appear.

Illumination refers to the amount of light falling on an object, while reflectance represents how much light is reflected back from the object’s surface. Together they determine image brightness.

To store a 256 × 256 image with 32 gray levels, each pixel requires 5 bits because 32 = 2⁵. Total bits required = 256 × 256 × 5 bits.

Connectivity defines how pixels are related to each other in an image. There are three main types: 4-connectivity, 8-connectivity, and mixed connectivity.

Contrast stretching is an image enhancement technique used to improve image visibility by expanding intensity values.

Dilation and erosion are morphological operations. Dilation increases object boundaries, while erosion shrinks them.

A noise model explains unwanted disturbances in images caused by sensors, transmission errors, or environmental factors.

Edge detection operators such as Sobel, Prewitt, and Canny are used to detect object boundaries in images.

Affine transformation is a geometric transformation that includes rotation, scaling, translation, and shearing of images.

Thresholding is a segmentation technique used to convert grayscale images into binary images by selecting a threshold value.

Questions (Section A)

What is a digital image and dynamic range?

Explain illumination and reflectance.

How many bits are needed to store a 256×256 image with 32 gray levels?

What is pixel connectivity?

Define contrast stretching.

SECTION – B

Attempt any five (Long Explanation)

Digital Image Processing is a method of manipulating images using computer algorithms to enhance quality or extract information. Its main stages include image acquisition, processing, and interpretation. It is widely used in medical imaging, satellite imaging, and security systems.

Sampling converts continuous images into discrete form, while quantization converts continuous intensity values into discrete levels. Homomorphic filtering is used to separate illumination and reflectance components to enhance images.

Histogram equalization is a technique used to improve contrast by redistributing pixel intensity values. It spreads out frequent intensity values to enhance image clarity.

Boundary extraction is used to identify object boundaries in an image using morphological operations. Structuring elements help define shapes during processing.

Noise is unwanted information present in images. Common noise types include Gaussian noise and impulse noise. Noise models help in designing restoration filters.

Geometric transformation changes image shape using mathematical operations such as scaling, rotation, and translation. Euclidean transformation preserves distances and angles.

Morphological operations such as dilation and erosion are used in opening and closing operations. Opening removes small objects, while closing fills small holes.

Image segmentation divides images into meaningful regions. Illumination and reflectance also play a major role in segmentation.

Questions (Section B)

What is digital image processing and its applications?

Explain sampling and quantization.

What is histogram equalization?

Define boundary extraction.

What is noise and noise model?

SECTION – C

Attempt any two (Detailed Explanation)

Digital image processing consists of multiple stages such as acquisition, enhancement, restoration, segmentation, representation, and interpretation. Each stage plays a role in transforming raw images into useful information.

Stereo imaging is a technique used to capture depth information by combining two images. Region filling is a method used to fill areas inside boundaries. Convex hull represents the smallest convex shape that contains an object.

Image restoration focuses on removing degradation such as blur and noise to recover the original image. The restoration process includes degradation model, restoration filter, and output image.