Principles of Digital Image Processing: Core Algorithms (Undergraduate Topics in Computer Science)

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Publisher ‏ : ‎ Springer; 2009th edition (April 2, 2009)
Language ‏ : ‎ English
Paperback ‏ : ‎ 344 pages
ISBN-10 ‏ : ‎ 1848001940
ISBN-13 ‏ : ‎ 978-1848001947
Item Weight ‏ : ‎ 1.96 pounds
Dimensions ‏ : ‎ 7.01 x 0.78 x 10 inches

Digital image processing is a key area of study in computer science, with applications ranging from medical imaging to facial recognition. In this post, we will delve into the core algorithms that form the foundation of digital image processing.

1. Image Enhancement: One of the fundamental tasks in digital image processing is enhancing the quality of an image. This can involve adjusting brightness and contrast, removing noise, and sharpening edges. Algorithms such as histogram equalization, adaptive filtering, and edge detection are commonly used for image enhancement.

2. Image Segmentation: Image segmentation involves dividing an image into regions or objects of interest. This is crucial for tasks such as object recognition and tracking. Algorithms like thresholding, region growing, and clustering are used for image segmentation.

3. Image Compression: With the increasing volume of digital images being generated, image compression has become essential for efficient storage and transmission. Lossless compression algorithms like Huffman coding and lossy compression algorithms like JPEG are commonly used in digital image processing.

4. Image Restoration: Image restoration aims to recover the original image from a degraded or noisy version. This can involve removing blurring, restoring missing details, and correcting distortions. Algorithms such as Wiener filtering, deconvolution, and inpainting are used for image restoration.

5. Feature Extraction: Feature extraction involves identifying and extracting relevant information from an image. This is crucial for tasks like object recognition, classification, and tracking. Algorithms like HOG (Histogram of Oriented Gradients), SIFT (Scale-Invariant Feature Transform), and SURF (Speeded-Up Robust Features) are commonly used for feature extraction.

These core algorithms form the backbone of digital image processing and are essential for understanding and implementing advanced image processing techniques. By mastering these principles, students can gain a solid foundation in digital image processing and apply their knowledge to real-world applications in various fields.
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