Author: Zen and the Art of Computer Programming
1 Introduction
In the field of machine learning, image processing is a very important direction. Simply put, image processing is a series of operations such as analysis, processing, and recognition of image data captured in the real world. Image processing can be applied in different fields, such as face recognition, object detection, image enhancement, super-resolution, etc. Research in the field of traditional computer vision generally focuses on feature extraction, classification, regression, and image registration. In recent years, with the rise of deep learning, image processing has become more and more popular. Deep learning frameworks such as TensorFlow, PyTorch, and MXNet provide powerful model capabilities, making research on image processing tasks develop rapidly. With the continuous development of hardware and the improvement of computing power, image processing tasks will rely more on intelligent algorithms and automation technologies. This article first introduces some basic concepts related to image processing, and explains the image processing methods and development trends in deep learning. Then, some commonly used image processing methods and their characteristics are introduced in detail, including convolutional neural network (CNN), unsupervised methods, pixel-level image processing methods, depth estimation methods and semantic segmentation methods. Finally, summarize the keywords involved in this article and the author's understanding of this article. Readers are welcome to provide valuable comments or suggestions to jointly promote the improvement of this article.
2. Basics of image processing
2.1 RGB color model
Each pixel uses three color channels of red, green and blue to describe its color, which is called the RGB color model. As shown below:
2.1.1 Why is there an RGB color model?
In the earliest days, the main purpose of image processing was to recognize the characteristics of objects, such as color, shape, texture, etc. Due to the limited range of color representation, the human eye can only recognize a limited number of colors. Therefore, using color differences as features of images can help machines recognize objects. However, the differences between the different colors are too small to distinguish subtle differences, such as yellow-green and orange-red. To solve this problem,