Digital image processing involves using computer algorithms to analyze and manipulate digital images. It includes techniques for image acquisition like capturing with cameras and scanning, then converting analog images to digital. Methods are described for enhancing images through color correction, noise reduction, and sharpening. Image restoration can filter blurred images and undo degradation from motion blur. Algorithms are presented for lossless and lossy image compression to reduce file sizes. Segmentation divides images into regions while feature extraction identifies edges, corners and blobs. Deep learning and pattern recognition can recognize and classify objects. Applications discussed are in medical imaging, robotics, and remote sensing.

1. Digital Image
Processing
G.samuel
G.samuel
Digital Image
Processing
Done by:
G.Samuel

2. Introduction to Digital
Image Processing
Digital image processing involves using computer algorithms to perform
image analysis and manipulation, with applications across various fields.

3. Image Acquisition and Digitization
High-Quality Capture
Techniques for capturing images through
cameras, scanners, and other devices.
Digital Conversion
Process of converting analog images into
digital form for computer processing.

4. Image Enhancement Techniques
1 Color Correction
Methods to adjust and balance color
tones for optimal visual impact.
2 Noise Reduction
Techniques to reduce unwanted
artifacts and improve image clarity.
3 Sharpness Enhancement
Algorithms to increase the clarity and detail of images.

5. Image Restoration Methods
Filtering Restores blurred or distorted images
through various filtering algorithms.
Deconvolution Techniques to reverse image degradation
caused by defocus or motion blur.

6. Image Compression Algorithms
Lossless Compression
Reduces file size without losing image
quality using encoding methods.
Lossy Compression
Minimizes file size by removing
redundant information, with some loss
of quality.

7. Image Segmentation and Feature
Extraction
8
Region-based Segmentation
Divides an image into regions based on color,
texture, or intensity.
20K
Feature Extraction
Identifies and extracts significant features such
as edges, corners, and blobs.

8. Object Recognition and Classification
Deep Learning
Utilizes neural networks to recognize and
classify objects within images.
Pattern Recognition
Identifies patterns within images using
statistical and machine learning
algorithms.

9. Applications of Digital Image
Processing
1
Medical Imaging
Used for diagnostic purposes, like X-
rays, MRI, and CT scans.
2
Robotics
Enables robots to perceive and interact with their
environment.
3
Remote Sensing
Used in satellite imagery for environmental monitoring
and resource management.

10. Thank You