VTUlive.com 1 1 https://vtupro.com IMAGE PROCESSING Subject Code : 10EC763 No. of Lecture Hrs/Week : 04 Total no. of Lecture Hrs. : 52 IA Marks : 25 Exam Hours : 03 Exam Marks : 100 PART - A UNIT - 1 DIGITAL IMAGE FUNDAMENTALS: What is Digital Image Processing. fundamental Steps in Digital Image Processing, Components of an Image processing system, elements of Visual Perception. 6 Hours UNIT - 2 Image Sensing and Acquisition, Image Sampling and Quantization, SomeBasic Relationships between Pixels, Linear and Nonlinear Operations. 6 Hours UNIT - 3 IMAGE TRANSFORMS: Two-dimensional orthogonal & unitary transforms, properties of unitary transforms, two dimensional discrete Fourier transform. 6 Hours UNIT - 4 Discrete cosine transform, sine transform, Hadamard transform, Haar transform, Slant transform, KL transform. 6 Hours PART - B UNIT - 5 IMAGE ENHANCEMENT: Image Enhancement in Spatial domain, SomeBasic Gray Level Trans -formations, Histogram Processing, Enhancement Using Arithmetic/Logic Operations. 6 Hours UNIT - 6 Basics of Spatial Filtering Image enhancement in the Frequency Domain filters, Smoothing Frequency Domain filters, Sharpening Frequency Domain filters, homomorphic filtering. 6 Hours UNIT - 7 Model of image degradation/restoration process, noise models, Restoration in the Presence of Noise, Only-Spatial Filtering Periodic Noise Reduction by Frequency Domain Filtering, Linear Position-Invariant Degradations, inverse filtering, minimum mean square error (Weiner) Filtering 10 Hours UNIT - 8 Color Fundamentals. Color Models, Pseudo color Image Processing., processing basics of full color image processing 6 Hours TEXT BOOK: 1. “Digital Image Processing”, Rafael C.Gonzalez and Richard E. Woods, Pearson Education, 2001, 2nd edition. REFERENCE BOOKS: 1. “Fundamentals of Digital Image Processing”, Anil K. Jain, Pearson Edun, 2001. 2. “Digital Image Processing and Analysis”, B. Chanda and D. Dutta Majumdar, PHI, 2003.
VTUlive.com 4 Image Processing 10EC763 2 https://vtupro.com Unit-1 Introduction What Is Digital Image Processing? An image may be defined as a two-dimensional function, f(x, y), where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point. When x, y, and the amplitude values of f are all finite, discrete quantities, we call the image a digital image. The field of digital image processing refers to processing digital images by means of a digital computer. Note that a digital image is composed of a finite number of elements, each of which has a particular location and value. These elements are referred to as picture elements, image elements, pels, and pixels. Pixel is the term most widely used to denote the elements of a digital image. Fundamental Steps in Digital Image Processing It is helpful to divide the material covered in the following chapters into the two broad categories defined in Section 1.1: methods whose input and output are images, and methods whose inputs may be images, but whose outputs are attributes extracted from those images..The diagram does not imply that every process is applied to an image. Rather, the intention is to convey an idea of all the methodologies that can be applied to images for different purposes and possibly with different objectives. Image acquisition is the first process acquisition could be as simple as being given an image that is already in digital form. Generally, the image acquisition stage involves preprocessing, such as scaling. Image enhancement is among the simplest and most appealing areas of digital image processing. Basically, the idea behind enhancement techniques is to bring out detail that is obscured, or simply to highlight Dept of ECE/SJBIT Page 1
VTUlive.com 5 Image Processing 3 10EC763 certain features of interest in an image. A familiar example of enhancement is when we increase the contrast of an image because “it looks better.” It is important to keep in mind that enhancement is a very subjective area of image processing Image restoration is an area that also deals with improving the appearance of an image. However, unlike enhancement, which is subjective, image restoration is objective, in the sense that restoration techniques tend to be based on mathematical or probabilistic models of image degradation. Enhancement, on the other hand, is based on human subjective preferences regarding what constitutes a “good” enhancement result. Color image processing is an area that has been gaining in importance because of the significant increase in the use of digital images over the Internet. fundamental concepts in color models and basic color processing in Dept of ECE/SJBIT Page 2 https://vtupro.com
VTUlive.com 6 Image Processing 4 10EC763 a digital domain. Color is used also in later chapters as the basis for extracting features of interest in an image. Wavelets are the foundation for representing images in various degrees of resolution. In particular, this material is used in this book for image data compression and for pyramidal representation, in which images are subdivided successively into smaller regions. Compression, as the name implies, deals with techniques for reducing the storage required to save an image, or the bandwidth required to transmit it. Although storage technology has improved significantly over the past decade, the same cannot be said for transmission capacity. This is true particularly in uses of the Internet, which are characterized by significant pictorial content. Image compression is familiar (perhaps inadvertently) to most users of computers in the form of image file extensions, such as the jpg file extension used in the JPEG(Joint Photographic Experts Group) image compression standard. Morphological processing deals with tools for extracting image components that are useful in the representation and description of shape. The material in this chapter begins a transition from processes that output images to processes that output image attributes, Segmentation procedures partition an image into its constituent parts or objects. In general, autonomous segmentation is one of the most difficult tasks in digital image processing. A rugged segmentation procedure brings the process a long way toward successful solution of imaging problems that require objects to be identified individually. On the other hand, weak or erratic segmentation algorithms almost always guarantee eventual failure. In general, the more accurate the segmentation, the more likely recognition is to succeed. Representation and description almost always follow the output of a segmentation stage, which usually is raw pixel data, constituting either the boundary of a region (i.e., the set of pixels separating one image region from another) or all the points in the region itself. In either case, converting the data to a form suitable for computer processing is necessary. The first Dept of ECE/SJBIT Page 3 https://vtupro.com