AKTU Image Processing · Important Questions 2026Source: ryzenstudy.com PYQs
BCS057 Image Processing Important Questions 2026 (AKTU)
Repeat analysis + most-asked questions, unit-wise
What we did: built from all 4 AKTU Image Processing papers (2021-22–2025-26): 94 questions grouped into 73 distinct questions, 16 of which were asked in more than one paper.
- of marks were repeat questions
- 45%
- of marks were repeat questions
- questions asked in 2+ papers
- 16
- questions asked in 2+ papers
- papers analysed (2021-22–2025-26)
- 4
- papers analysed (2021-22–2025-26)
- units, mapped to current syllabus
- 5
- units, mapped to current syllabus
How much of the paper repeats?
- Repeats are real: when we checked each newer BCS057 paper against the papers before it, on average 45% of its marks were questions already asked in an earlier paper.
- 2024-25 paper: 43% of marks were repeats (2 of 8 repeated questions were in our top list).
- 2025-26 paper: 48% of marks were repeats (4 of 10 repeated questions were in our top list).
- Topic weightage is a guide, not a guarantee: our Tier 1 + Tier 2 topics covered 56% of the next paper's marks — picking the same number of topics at random would have covered about 45%.
Across 10 subjects and 23 papers we tested, 43% of marks repeated earlier questions on average.
1.Unit-wise most-asked questions
Ranked by how many papers asked them, then by how recently. Click a paper to open it.
Unit 1: DIGITAL IMAGE FUNDAMENTALS
15% of past marks| Question | Asked |
|---|---|
| What do you mean by image processing? Explain the steps of image processing with the help of block diagram. | Asked 3× |
| Define image sampling and quantization. | Asked 2× |
| What is the Discrete Fourier Transform (DFT), and how is it used to transform an image into the frequency domain for processing? | Asked 2× |
| Discuss the fundamentals of color images, including how color is represented digitally in RGB and HSI models | Asked 2× |
Unit 2: IMAGE ENHANCEMENT
23% of past marks| Question | Asked |
|---|---|
| Explain homomorphic filtering. Compute the morphological opening of a binary image (see original paper). | Asked 3× |
| Differentiate Correlation and Convolution with 1-D function and a filter example. | Asked 2× |
| Explain Gaussian low pass filter. 7 marks2025-26 Q2(c) | Asked 1× |
| Explain histogram specification technique with suitable example. 7 marks2025-26 Q2(b) | Asked 1× |
Unit 3: IMAGE RESTORATION
20% of past marks| Question | Asked |
|---|---|
| What is Image Restoration? Draw and explain the basic block diagram of the restoration process. | Asked 2× |
| Describe Wiener filtering and how it is used to minimize noise in image restoration while preserving details | Asked 2× |
| Explain mean and order statistic filters with suitable example. 7 marks2025-26 Q6(b) | Asked 1× |
| What is noise in digital images? 2 marks2025-26 Q1(d) | Asked 1× |
Unit 4: IMAGE SEGMENTATION
22% of past marks| Question | Asked |
|---|---|
| Explain erosion and dilation with suitable example. | Asked 4× |
| Explain edge detection and edge linking. Also differentiate between edge detection and edge linking. | Asked 2× |
| Describe the thresholding technique used in image segmentation and how it is used to separate objects from the background | Asked 2× |
| Explain watershed segmentation algorithm in detail | Asked 2× |
Unit 5: IMAGE COMPRESSION AND RECOGNITION
20% of past marks| Question | Asked |
|---|---|
| Discuss the need for data compression. Also explain run length encoding algorithm in detail. | Asked 3× |
| Differentiate between JPEG and MPEG standard in detail. | Asked 2× |
| What is run length encoding? | Asked 2× |
| Explain the process of boundary description and how it helps in identifying object boundaries in image recognition | Asked 2× |
2.Topic weightage: which topics carry the marks
Blue = Tier 1 + Tier 2 (topics that carried ~80% of past marks). Grey = the rest. It shows where marks have gone, not a promise of what comes next.
Image Degradation Model · U3
7.1% (cum 7%)
Steps in Digital Image Processing · U1
6.3% (cum 13%)
Erosion and Dilation · U4
5.2% (cum 19%)
Need for Data Compression · U5
5.1% (cum 24%)
Basics of Spatial Filtering · U2
5% (cum 29%)
Gray Level Transformations · U2
4.8% (cum 33%)
Edge Detection · U4
4.5% (cum 38%)
Huffman Coding · U5
4.2% (cum 42%)
Homomorphic and Color Image Enhancement · U2
4.2% (cum 46%)
JPEG Standard · U5
4.1% (cum 51%)
Histogram Processing · U2
3.5% (cum 54%)
Band Reject and Band Pass Filters · U3
3.3% (cum 57%)
Wiener Filtering · U3
3.2% (cum 61%)
Mean Filters · U3
2.9% (cum 63%)
Boundary Representation and Description · U5
2.7% (cum 66%)
RGB and HSI Models · U1
2.7% (cum 69%)
DFT and DCT Transforms · U1
2.2% (cum 71%)
Watershed Segmentation Algorithm · U4
2.2% (cum 73%)
Region Based Segmentation · U4
2.1% (cum 75%)
Ideal, Butterworth and Gaussian Filters · U2
2.1% (cum 77%)
Image Sampling and Quantization · U1
2.1% (cum 79%)
Thresholding · U4
2% (cum 81%)
Dam Construction · U4
1.6% (cum 83%)
Region Splitting and Merging · U4
1.5% (cum 85%)
Introduction to Fourier Transform · U2
1.4% (cum 86%)
Run Length Encoding · U5
1.3% (cum 87%)
Frequency Domain Smoothing Filters · U2
1.3% (cum 89%)
Order Statistics Filters · U3
1.3% (cum 90%)
Region Growing · U4
1.3% (cum 91%)
Pattern Classes and Matching Recognition · U5
1.3% (cum 92%)
Sharpening Spatial Filtering · U2
1.2% (cum 94%)
Edge Linking via Hough Transform · U4
1.2% (cum 95%)
2D Mathematical Preliminaries · U1
0.9% (cum 96%)
MPEG Standard · U5
0.9% (cum 97%)
Noise Models and Properties · U3
0.7% (cum 97%)
Elements of Visual Perception · U1
0.7% (cum 98%)
Regional Descriptors and Texture · U5
0.7% (cum 99%)
Notch Filters · U3
0.6% (cum 99%)
Image Sensing and Acquisition · U1
0.5% (cum 100%)
Inverse Filtering · U3
0.5% (cum 100%)
| Topic | Papers | Share |
|---|---|---|
| Image Degradation Model · Unit 3 · Tier 1 | 4/4 | 7.1% |
| Steps in Digital Image Processing · Unit 1 · Tier 1 | 3/4 | 6.3% |
| Erosion and Dilation · Unit 4 · Tier 1 | 4/4 | 5.2% |
| Need for Data Compression · Unit 5 · Tier 1 | 3/4 | 5.1% |
| Basics of Spatial Filtering · Unit 2 · Tier 1 | 3/4 | 5% |
| Gray Level Transformations · Unit 2 · Tier 1 | 2/4 | 4.8% |
| Edge Detection · Unit 4 · Tier 1 | 4/4 | 4.5% |
| Huffman Coding · Unit 5 · Tier 1 | 2/4 | 4.2% |
| Homomorphic and Color Image Enhancement · Unit 2 · Tier 1 | 3/4 | 4.2% |
| JPEG Standard · Unit 5 · Tier 1 | 3/4 | 4.1% |
| Histogram Processing · Unit 2 · Tier 2 | 1/4 | 3.5% |
| Band Reject and Band Pass Filters · Unit 3 · Tier 2 | 2/4 | 3.3% |
| Wiener Filtering · Unit 3 · Tier 2 | 3/4 | 3.2% |
| Mean Filters · Unit 3 · Tier 2 | 3/4 | 2.9% |
| Boundary Representation and Description · Unit 5 · Tier 2 | 2/4 | 2.7% |
| RGB and HSI Models · Unit 1 · Tier 2 | 2/4 | 2.7% |
| DFT and DCT Transforms · Unit 1 · Tier 2 | 2/4 | 2.2% |
| Watershed Segmentation Algorithm · Unit 4 · Tier 2 | 2/4 | 2.2% |
| Region Based Segmentation · Unit 4 · Tier 2 | 2/4 | 2.1% |
| Ideal, Butterworth and Gaussian Filters · Unit 2 · Tier 2 | 2/4 | 2.1% |
| Image Sampling and Quantization · Unit 1 · Tier 2 | 2/4 | 2.1% |
| Thresholding · Unit 4 · Tier 2 | 2/4 | 2% |
| Dam Construction · Unit 4 · Tier 3 | 1/4 | 1.6% |
| Region Splitting and Merging · Unit 4 · Tier 3 | 1/4 | 1.5% |
| Introduction to Fourier Transform · Unit 2 · Tier 3 | 2/4 | 1.4% |
| Run Length Encoding · Unit 5 · Tier 3 | 2/4 | 1.3% |
| Frequency Domain Smoothing Filters · Unit 2 · Tier 3 | 1/4 | 1.3% |
| Order Statistics Filters · Unit 3 · Tier 3 | 1/4 | 1.3% |
| Region Growing · Unit 4 · Tier 3 | 1/4 | 1.3% |
| Pattern Classes and Matching Recognition · Unit 5 · Tier 3 | 1/4 | 1.3% |
| Sharpening Spatial Filtering · Unit 2 · Tier 3 | 1/4 | 1.2% |
| Edge Linking via Hough Transform · Unit 4 · Tier 3 | 1/4 | 1.2% |
| 2D Mathematical Preliminaries · Unit 1 · Tier 3 | 1/4 | 0.9% |
| MPEG Standard · Unit 5 · Tier 3 | 1/4 | 0.9% |
| Noise Models and Properties · Unit 3 · Tier 3 | 1/4 | 0.7% |
| Elements of Visual Perception · Unit 1 · Tier 3 | 1/4 | 0.7% |
| Regional Descriptors and Texture · Unit 5 · Tier 3 | 1/4 | 0.7% |
| Notch Filters · Unit 3 · Tier 3 | 1/4 | 0.6% |
| Image Sensing and Acquisition · Unit 1 · Tier 3 | 1/4 | 0.5% |
| Inverse Filtering · Unit 3 · Tier 3 | 1/4 | 0.5% |
3.Paper pattern (2025-26)
| Section | Questions | Rule |
|---|---|---|
| A | 7 × 2 marks | Attempt all |
| B | 5 × 7 marks | Attempt any 3 |
| C | 10 × 7 marks | Attempt any 1 |
4.In the syllabus, never asked yet
Low priority, but read them once — a new paper can always start here.
- Image Processing Components · U1
- Relationships Between Pixels · U1
- Color Image Fundamentals · U1
- Smoothing Spatial Filtering · U2
- Frequency Domain Sharpening Filters · U2
- Adaptive Filters · U3
- Optimum Notch Filtering · U3
- Morphological Watershed Concepts · U4
- Shift Codes and Arithmetic Coding · U5
- Fourier Descriptors · U5
5.Study order
- 1Image Degradation Model (Unit 3, Tier 1)
- 2Steps in Digital Image Processing (Unit 1, Tier 1)
- 3Erosion and Dilation (Unit 4, Tier 1)
- 4Need for Data Compression (Unit 5, Tier 1)
- 5Basics of Spatial Filtering (Unit 2, Tier 1)
- 6Gray Level Transformations (Unit 2, Tier 1)
- 7Edge Detection (Unit 4, Tier 1)
- 8Huffman Coding (Unit 5, Tier 1)
- 9Homomorphic and Color Image Enhancement (Unit 2, Tier 1)
- 10JPEG Standard (Unit 5, Tier 1)
- 11Histogram Processing (Unit 2, Tier 2)
- 12Band Reject and Band Pass Filters (Unit 3, Tier 2)
- 13Wiener Filtering (Unit 3, Tier 2)
- 14Mean Filters (Unit 3, Tier 2)
- 15Boundary Representation and Description (Unit 5, Tier 2)
- 16RGB and HSI Models (Unit 1, Tier 2)
- 17DFT and DCT Transforms (Unit 1, Tier 2)
- 18Watershed Segmentation Algorithm (Unit 4, Tier 2)
- 19Region Based Segmentation (Unit 4, Tier 2)
- 20Ideal, Butterworth and Gaussian Filters (Unit 2, Tier 2)
- 21Image Sampling and Quantization (Unit 1, Tier 2)
- 22Thresholding (Unit 4, Tier 2)
FAQ
Which units of BCS057 carry the most marks?
Unit 2 (IMAGE ENHANCEMENT, 23% of past marks) and Unit 4 (IMAGE SEGMENTATION, 22% of past marks).
Do AKTU Image Processing questions repeat?
Yes. On average 45% of the marks in each newer BCS057 paper came from questions already asked in an earlier paper, and 16 questions have appeared in more than one paper.
How is this list made?
Every question from the 4 papers (2021-22–2025-26) was mapped to the current AKTU syllabus and grouped with the same question from other years. Questions are ranked by how many papers they appeared in, then by how recently.
Study this subject
Note: this is based on past AKTU papers, not a guarantee. Questions are mapped to the current syllabus; topics that are no longer in the syllabus are left out.