Spatial-domain processing(Histogram Processing) II

Описание: Mastering Image Processing: Gamma Correction, Normalization, and Histogram Equalization

In this comprehensive Tutorial, delve into the principles of image processing, starting with the effects of gamma correction and normalization. Learn how to normalize images between 0 and 1, and explore different histogram equalization methods, including global and local equalization. Understand how to handle specialized cases like dark images and how to avoid making images too bright by using custom CDFs. The video further covers convolution operations, various filtering techniques for noise removal, image smoothing, and sharpening, including practical examples and the importance of selecting the appropriate filter for specific applications. Advanced concepts, such as flipping filters for convolution, dealing with salt and pepper noise, and segmentation in deep learning, are also discussed, highlighting the trade-offs and limitations inherent to each method.

00:00 Introduction to Power and Gamma Values
00:31 Normalization Techniques Explained
01:47 Challenges with Histogram Equalization
02:36 Specifying and Transforming Histograms
05:35 Practical Example of Histogram Equalization
09:42 Limitations and Advanced Techniques
10:54 Introduction to Local and Global Information
11:14 Segmentation and Equalization Techniques
12:40 Understanding Noise and Its Types
12:59 Introduction to Spatial Filtering
14:54 Linear and Non-Linear Filters
16:57 Convolution Process Explained
20:10 Impulse Function and Its Properties