Summary of "Digital Image Processing" Chapter 1: Introduction

　　最近在自学《数字图像处理》这本书，发现如果不及时整理、回顾的话，就总是学到后面忘了前面。。。所以，从今天开始，我准备每学完一部分内容就把其中重要的部分整理一下，也从此开始培养在学习各种技术、知识过程中不断及时总结重点的习惯。同时，我也无私地把这些总结提供给大家作为对某项技术的大致了解或者复习等的资料，如果能够用的上的话。

　　注：章节编号及内容完全与书本相同，但章节内仅含我自学过的并且个人认为重要的内容，故其编号不一定与书本相同。（所有的“自学整理”均适用。）

Chapter 1: Introduction

1.1 What is Digital Image Processing

　　Image processing not only consists of improving the quility of photos or analyzing them, which is not what I have thought before, but encompasses many aspects including the acquisition, denoising, enhancement and compression of an image, and some superior aspects like image segmentation and object recognition, etc.

　　Besides, here, the word "image", not "photo" or "picture", also reveals something to us that the input of image processing is not only the optic waves, but the whole range of electromagnetic waves containing Gamma-Ray, X-Ray, Ultraviolet Band, Infrared Band, Microwave Band, Radio Band, etc. And this is also what makes image processing a wide utilized technology like in medical fields, 3D model acquisition and satellite remote sensing, etc.

1.2 Fundamental Steps in Digital Image Processing

1.2.1 Processes whose outputs generally are images

Image Acquisition

 　　Use a sensor to emit electromagnetic waves -> Absorb the waves reflected by the object -> Sampling and quantization -> Image obtained

Image Enhancement

 　　The process, such as contrast manipulation and image sharpening, which is subjective, of manipulating an image so that the result is more suitable than the original for specific application. This process can be done in spatial domain or frequency domain using the Fourier Transform.

Image Restoration

 　　An area that also deals with improving the apearance of an image. However, unlike enhancement, it is objective, in the sense that restoration techniques tend to be based on mathematical or probabilistic models of image degradation. It can be generalized by the word "denoising".

Color Image Processing

 　　An area that deals with processing color images using different color models, such as RGB for displaying, CMYK for printing and HSI, etc, which, besides what have been contained in the intensity transformation, has more operations oriented on colors, like hue and saturation revising. 

Wavelets

 　　Wavelets are the foundation for representing images in various degrees of resolution. It is also used for image data compression and for pyramidal representation, in which images are subdivided successively into smaller regions.

Compression

 　　Compression is the area using various coding method to reduce the data redundancy in an image in order to minimize the data used to represent an image, thus improving the process of transmission and displaying the image. 

1.2.2 Processes whose outputs generally are image attributes

Morphological processing

 　　Morphological processing deals with tools for extracting image components that are useful in the representation and description of shape.

Segmentations

 　　Segmentation procedures partition an image into its constituent parts or objects.

Representation and description

　　Representation and description almost always follow the output of a segmentation stage.

　　Choosing a representation is part of the solution for transforming raw data into suitable for subsequent computer processing.

　　Description, also called fearture selection, deals with extracting attributes that result in some quantitative information of interest or are basic for differentiating one class of objects from another.

Recognition

 　　Recognition is the process that assigns a label to an object based on its descriptors.