RTU Kota B.Tech 6th Semester Digital Image Processing Question Paper 2025 (CSE/IT/AI)

RTU Digital Image Processing 2025 Paper Review

Preparing for the Rajasthan Technical University B.Tech Digital Image Processing exam requires a solid grasp of 2D signal mathematics, matrix convolutions, and visual perception limits. For Artificial Intelligence, Computer Science, and IT students, this subject acts as the direct precursor to computer vision and deep learning models. You cannot build a robust facial recognition system, optimize autonomous driving cameras, or process medical scans without understanding how algorithms manipulate pixel intensities mathematically.

The 2025 paper evaluates your capability to apply $3 \times 3$ spatial masks, compute Fourier transform thresholds, and execute morphological operations. Publishing this specific 6th-semester paper review directly to exam-support.in provides your users exactly what they need to understand how examiners construct matrix problems and distribute marks across the mathematical modules. This targeted preparation strategy helps approach the exam confidently, Jaiprakash.

Understanding the Exam Pattern

The RTU theory examination is a three-hour paper worth 70 marks. The paper features three distinct sections designed to evaluate both basic imaging definitions and complex matrix calculations.

• Part A: This section contains ten compulsory questions worth two marks each. You must define terms like spatial resolution, state the difference between 4-adjacency and 8-adjacency, define the Weber ratio, or write the formula for 2D discrete convolution under 30 words.
• Part B: You will find seven questions here. You must answer five of them. Each question is worth four marks. Your answers require executing short calculations like finding the median value for a $3 \times 3$ sub-image, explaining the difference between spatial and frequency domain filtering, or outlining the steps of histogram equalization.
• Part C: This section offers five major questions. You need to answer three. Each question carries ten marks. These require you to perform complete histogram specification for a given probability distribution, derive the 2D Fast Fourier Transform (FFT) properties, or execute edge detection using Sobel operators followed by the Hough transform for line linking.

Core Topics Evaluated in the Paper

The 2025 question paper covers several critical modules that establish the mathematical rules for visual computing. Focus your study time on these specific areas to maximize your score.

Digital Image Fundamentals and Enhancement

This module evaluates your understanding of basic pixel relationships and intensity transformations. You must master contrast stretching, log transformations, and bit-plane slicing. The heaviest mathematical focus is on Histogram Equalization. Practice calculating the running sum of probabilities and mapping original gray levels to equalized levels. Expect numerical problems providing a $5 \times 5$ image with an 8-level gray scale where you must compute the new equalized image matrix.

Spatial and Frequency Domain Filtering

Filtering removes noise or sharpens edges. In the spatial domain, you must understand how to apply convolution masks. Practice applying a $3 \times 3$ averaging filter or a Laplacian filter to a specific pixel coordinate. In the frequency domain, study the transfer functions for Ideal, Butterworth, and Gaussian Low Pass and High Pass filters. You must know how the cutoff frequency $D_0$ affects ringing artifacts.

Image Restoration and Noise Models

Images degrade due to sensor heat or transmission errors. You must memorize the probability density functions for different noise models, specifically Gaussian noise and Salt-and-Pepper noise. Understand why the Median filter is highly effective at removing impulse (Salt-and-Pepper) noise while preserving sharp edges, whereas a Mean filter blurs them. Study the theoretical foundation of the Wiener filter for minimizing the mean square error of the restored image.

Morphological Image Processing

This module uses set theory to extract image components like boundaries and skeletons. You must master the two fundamental operations: Dilation and Erosion. Understand how a structuring element (like a $3 \times 3$ square or cross) interacts with the image pixels. Practice executing the Opening (erosion followed by dilation) and Closing (dilation followed by erosion) operations to smooth contours and fill narrow gaps in binary images.

Image Segmentation and Compression

Segmentation isolates objects of interest. Focus on point, line, and edge detection masks (Prewitt, Sobel, Roberts). Study global and adaptive thresholding techniques. For compression, you must differentiate between coding redundancy, interpixel redundancy, and psychovisual redundancy. Practice building Huffman coding trees for a given set of pixel probability values to calculate the final compression ratio.

Answer Writing Strategy for High Marks

RTU evaluators look for neat incremental calculation tables, explicitly stated matrix equations, and clean graphical plots. Use a blue pen for text explanations and calculation lines. Use a black pen and ruler for drawing spatial masks, image coordinate grids, and compression trees.

In Part A, answer directly. If a question asks for the definition of quantization, state clearly that it is the process of converting a continuous amplitude value into a discrete digital value, which directly determines the number of gray levels in the image.

In Part B, use clear computation grids. When executing a spatial filter calculation, draw the original pixel grid, draw the $3 \times 3$ filter mask explicitly next to it, and show the arithmetic sum of products before writing the final updated pixel value.

In Part C, precision in algorithms is critical. When solving a ten-mark histogram equalization problem, draw a neat table with columns for: Gray Level ($r_k$), Number of Pixels ($n_k$), Probability $p(r_k)$, Cumulative Probability, Scaled Value, and Rounded Equalized Value ($s_k$). Finally, plot the original histogram versus the equalized histogram using clear axes.

Time Management During the Exam

Allocate exactly 20 minutes to Part A. Spend 40 minutes addressing the five short-answer questions in Part B. Reserve the remaining 120 minutes for the three long-answer questions in Part C. Computing matrix convolutions, calculating cumulative probabilities for histograms, and executing multi-step Huffman coding requires steady focus and significant writing time to prevent arithmetic mistakes. This distribution guarantees you 40 minutes per major question, giving you time to double-check your fractional calculations. Use the final 10 minutes to verify your question numbering, ensure all matrix axes are labeled, and check that your pixel coordinates follow the correct row-column progression.