{"header":"

Test Title/Subject Name

Total Marks: 10

Total Time: 1/2 Hours

All questions are mandatory unless stated otherwise.
Read each question carefully before answering.
Use a blue or black pen for writing answers.
Do not use any electronic gadgets.
This Question paper is generated by AI.

","questions":[{"id":"a0070671-87f5-4888-b3dc-82be25b9c68b","questionText":"

What is the primary purpose of digital image processing?

","options":["Modify images for human interpretation","Enhance images for machine understanding","Both a and b","None of the above"],"correctAnswer":"Both a and b","explanation":"

Digital image processing serves both human and machine applications by improving image quality and extracting meaningful information.

","marks":"1","type":"obj","image":null},{"id":"4543f7ec-8135-48fc-922b-1b8ec96daee8","questionText":"

Which of the following represents a grayscale image?

","options":["CMYK","HSV","RGB","Single-channel intensity values"],"correctAnswer":"Single-channel intensity values","explanation":"

A grayscale image contains only intensity information, whereas RGB, CMYK, and HSV contain multiple color channels.

","marks":"1","type":"obj","image":null},{"id":"d71c8c79-4099-4c57-b60b-9407dacf66e9","questionText":"

Histogram equalization is used to:

","options":["Reduce noise in an image","Improve contrast","Detect edges","Reduce image size"],"correctAnswer":"Improve contrast","explanation":"

Histogram equalization enhances image contrast by spreading out intensity values more evenly.

","marks":"1","type":"obj","image":null},{"id":"a9342a50-e96b-459c-ab61-18507ff5a322","questionText":"

A high-pass filter in spatial filtering enhances:

","options":["Edges and details","Smooth regions","Color information","Image size"],"correctAnswer":"Edges and details","explanation":"

High-pass filtering removes low-frequency components, highlighting sharp transitions such as edges.

","marks":"1","type":"obj","image":null},{"id":"dd5d3923-314f-48a4-82ac-56cedbfd0f46","questionText":"

Which of the following is NOT a spatial domain filtering technique?

","options":["Gaussian filter","Median filter","Fourier transform","Sobel operator"],"correctAnswer":"Fourier transform","explanation":"

Fourier Transform is a frequency domain technique, whereas the others operate in the spatial domain.

","marks":"1","type":"obj","image":null},{"id":"e066201c-976f-4f18-a483-75082604ae1a","questionText":"

The 2D Fourier Transform is used to:

","options":["Blur an image","Convert an image to frequency domain","Remove noise","Enhance brightness"],"correctAnswer":"Convert an image to frequency domain","explanation":"

The 2D Fourier Transform decomposes an image into its frequency components, useful for various filtering operations.

","marks":"1","type":"obj","image":null},{"id":"e26ac3e4-5d86-4578-841c-bd946c589fc4","questionText":"

Low-pass filters are used in image processing to:

","options":["Sharpen images","Detect edges","Remove high-frequency noise","Increase contrast"],"correctAnswer":"Remove high-frequency noise","explanation":"

Low-pass filters smooth an image by removing high-frequency details, reducing noise and blurring edges.

","marks":"1","type":"obj","image":null},{"id":"22fa5171-59e3-482b-8e19-0b4d59b68739","questionText":"

Wiener filtering is primarily used for:

","options":["Histogram equalization","Edge detection","Image restoration","Image segmentation"],"correctAnswer":"Image restoration","explanation":"

Wiener filtering is an adaptive filter that reduces noise while preserving details in an image.

","marks":"2","type":"obj","image":null},{"id":"de03e2a5-6e1f-40da-ac22-7cc45176180c","questionText":"

Which noise model is best suited for impulse noise?

","options":["Gaussian noise","Salt and pepper noise","Speckle noise","Poisson noise"],"correctAnswer":"Salt and pepper noise","explanation":"

Salt and pepper noise consists of random white and black pixels, commonly removed using median filtering.

","marks":"1","type":"obj","image":null},{"id":"d82d3e43-123c-4db4-843f-1582686ee6bd","questionText":"

Direct inverse filtering is limited due to:

","options":["Lack of speed","Sensitivity to noise","Inaccuracy in spatial domain","High computational cost"],"correctAnswer":"Sensitivity to noise","explanation":"

Inverse filtering amplifies noise, making it impractical for real-world image restoration applications.

","marks":"1","type":"obj","image":null}],"totalMarks":"10","totaltime":"0.5"}