8.C] Explain the fundamental steps in image processing.
Answer:
- Image Acquisition
- Capturing an image using a digital camera, scanner, or importing an existing image into a computer.
- Obtain a digital representation of a real-world scene or object for further processing.
- Example: Scanning a photograph or taking a picture with a digital camera.
2. Image Enhancement
- Improving the visual quality of an image to make it more suitable for analysis or viewing.
- Increase contrast, reduce noise, and remove artifacts to enhance clarity and details.
- Common Techniques:
- Contrast Adjustment: Enhancing the difference between light and dark areas.
- Noise Reduction: Using filters to smooth out unwanted noise.
- Artifact Removal: Correcting issues caused by imaging devices.
- Example: Applying histogram equalization to improve contrast in a low-light photo.
3. Image Restoration
- Removing degradation or distortions from an image to restore its original quality.
- Correct blurring, noise, and other distortions that affect the image quality.
- Common Techniques:
- Deblurring: Using algorithms to reverse the effects of blurriness.
- Denoising: Reducing random noise while preserving important features.
- Example: Applying a deblurring filter to a blurred scanned document.
4. Image Segmentation
- Dividing an image into distinct regions or segments that correspond to specific objects or features.
- Isolate and identify different parts of the image for detailed analysis.
- Common Techniques:
- Thresholding: Creating binary images to separate objects from the background.
- Region Growing: Grouping adjacent pixels with similar properties.
- Example: Segmenting a medical image to identify different tissues or organs.
5. Image Representation and Description
- Representing an image in a form that can be analyzed and manipulated by a computer, and describing its features in a compact way.
- Convert the image into a format that facilitates analysis and feature extraction.
- Common Techniques:
- Feature Extraction: Identifying key attributes or patterns in the image.
- Image Encoding: Converting the image into a data format suitable for processing.
- Example: Representing an image using feature vectors for object recognition.
6. Image Analysis
- Using algorithms and mathematical models to extract meaningful information from an image.
- Recognize objects, detect patterns, and quantify features for interpretation.
- Common Techniques:
- Object Recognition: Identifying and classifying objects within the image.
- Pattern Detection: Finding recurring patterns or anomalies.
- Example: Using machine learning algorithms to recognize faces in an image.
7. Image Synthesis and Compression
- Generating new images or compressing existing images to reduce storage and transmission requirements.
- Create new images based on existing data or reduce the size of image files.
- Common Techniques:
- Image Compression: Reducing file size using techniques like JPEG or PNG.
- Image Synthesis: Creating new images or enhancing existing ones.
- Example: Compressing a high-resolution image to save space on a digital device.