AI Lesson 82 – Image Processing Basics | Dataplexa

AI Course

I. AI Foundations
1. Introduction to AI 2. History of AI 3. AI vs ML vs DL 4. Search Algorithms 5. Intelligent Agents 6. Informed & Uninformed Search 7. Constraint Satisfaction 8. Adversarial Search 9. Logic in AI 10. Planning in AI 11. Knowledge Representation 12. Probabilistic AI 13. Bayesian Networks 14. Markov Models 15. AI Ethics 16. AI Applications 17. Intro to PyTorch 18. Intro to TensorFlow 19. AI Workflows 20. Data for AI
II. Machine Learning
21. Introduction to ML 22. ML Workflow 23. Supervised Learning 24. Unsupervised Learning 25. Reinforcement Learning 26. Linear Regression 27. Logistic Regression 28. SVM 29. Decision Trees 30. Random Forest 31. Gradient Boosting 32. XGBoost 33. K-Means 34. Hierarchical Clustering 35. Dimensionality Reduction 36. Feature Engineering 37. Feature Selection 38. Model Evaluation 39. Overfitting vs Underfitting 40. Cross Validation
III. Deep Learning
41. Intro to Deep Learning 42. Neural Networks 43. Activation Functions 44. Backpropagation 45. Loss & Optimizers 46. Regularization 47. CNN Basics 48. CNN Architectures 49. RNN, LSTM, GRU 50. Seq2Seq 51. Transformers 52. Attention 53. Positional Encoding 54. Training DL 55. Hyperparameter Tuning 56. Transfer Learning 57. Autoencoders 58. GAN Basics 59. GAN Applications 60. Model Serving
IV. Natural Language Processing
61. Intro to NLP 62. Text Processing 63. Tokenization 64. Stopwords & Lemmatization 65. BoW & TF-IDF 66. Word Embeddings 67. Sentence Embeddings 68. NLP Classification 69. Sequence Modeling 70. RNN in NLP 71. Transformers in NLP 72. BERT Basics 73. BERT Fine-tuning 74. RoBERTa & DistilBERT 75. Sentiment Analysis 76. NER 77. Machine Translation 78. Text Generation 79. NLP Evaluation 80. NLP Use Cases
V. Computer Vision
81. Intro to CV 82. Image Processing 83. Filters & Edges 84. Convolutions 85. Object Detection 86. Object Tracking 87. Image Segmentation 88. Face Recognition 89. Feature Detection 90. OCR 91. Image Augmentation 92. Generative Vision 93. Vision Transformers 94. Multimodal Models 95. CV Use Cases
VI. LLM Engineering
96. Intro to LLMs 97. Tokenization 98. LLM Architecture 99. Pretraining 100. Fine-tuning 101. RLHF 102. Prompt Engineering 103. Embedding Models 104. Vector Databases 105. LLM Agents 106. Guardrails & Safety 107. AI in Production 108. End-to-End Project

Lesson 82: Image Processing Basics

Image processing is the foundation of computer vision. Before a model can recognize objects or understand scenes, images must often be cleaned, enhanced, or transformed.

These operations improve image quality and make important features easier for algorithms to detect.

Real-World Connection

Camera apps enhance photos automatically, medical scans are clarified for doctors, and security footage is improved for visibility. All of this uses image processing.

Even before deep learning models are applied, image processing prepares data for better results.

What Is Image Processing?

Image processing involves manipulating images to improve visual quality or extract useful information. It works directly on pixel values.

  • Noise reduction
  • Contrast enhancement
  • Edge detection
  • Geometric transformations

Reading and Displaying an Image

Let’s start with loading and displaying an image using OpenCV. 


import cv2

image = cv2.imread("image.jpg")
cv2.imshow("Original Image", image)
cv2.waitKey(0)
cv2.destroyAllWindows()

This code reads an image from disk and displays it in a window.

Grayscale Conversion

Color images contain three channels (RGB). Converting to grayscale simplifies processing and reduces computation. 


gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow("Grayscale Image", gray)
cv2.waitKey(0)
cv2.destroyAllWindows()

Why Grayscale Is Useful

Many algorithms focus on intensity changes rather than color. Grayscale images highlight structure, edges, and shapes more clearly.

Image Resizing

Images often need to be resized for faster processing or model input requirements. 


resized = cv2.resize(image, (300, 300))
cv2.imshow("Resized Image", resized)
cv2.waitKey(0)
cv2.destroyAllWindows()

Understanding Pixel Values

Each pixel has an intensity value. In grayscale images, values range from 0 (black) to 255 (white).

Image processing modifies these values mathematically to achieve desired effects.

Common Image Processing Operations

  • Blurring to remove noise
  • Thresholding to separate objects
  • Sharpening to enhance edges

Practice Questions

Practice 1: What is the main goal of image processing?



Practice 2: Which image format reduces color information?



Practice 3: What does image processing operate on directly?



Quick Quiz

Quiz 1: Which task improves image quality?





Quiz 2: Why is grayscale conversion used?





Quiz 3: Which operation changes image dimensions?





Coming up next: Filters & Edge Detection — how machines detect boundaries and important structures in images.

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