3.1-3.4_gray-level transformations(Image Enhancement in Spatial Domain).pptVIP

Let’s go! Chapter 1 Introduction to digital image processing ※ ※ Chapter2 Digital Image fundamentals ※ ※ Chapter3-5 Image Enhancement Chapter 6 Image Restoration Chapter 7 Image compression ※ Chapter 8-10 Image Analysis Chapter 3-5  1. Principle Objective of Image Enhancement To process an image so that the result will be more suitable than the original image for a specific application. Good images For human visual The visual evaluation of image quality is a highly subjective process. It is hard to standardize the definition of a good image. For machine perception The evaluation task is easier. A good image is one which gives the best machine recognition results. 2. Uses of enhancement techniques Debase of contrast in the acquire of image / for specific application Weakening of signals Blurry and distortion of image because of noise 3. Image enhancement approaches Spatial Domain : Techniques are based on direct manipulation of pixels in an image Frequency Domain : Techniques are based on modifying the Fourier transform of an image Chapter 3 Image enhancement in Spatial Domain Spatial Domain Procedures that operate directly on pixels. g(x,y) = T[f(x,y)] where f(x,y) is the input image g(x,y) is the processed image T is an operator on f defined over some neighborhood of (x,y) Mask/Filter Neighborhood of a point (x,y) can be defined by using a square/rectangular or circular subimage area centered at (x,y) The center of the subimage is moved from pixel to pixel starting at the top of the corner 1) Point Processing Neighborhood = 1x1 pixel g depends on only the value of f at (x,y) T = gray level (or intensity or mapping) transformation function s = T(r) Where r = gray level of f(x,y) s = gray level of g(x,y) 2) Mask Processing or Filter Neighborhood is bigger than 1x1 pixel Use a function of the values of f in a predefined neighborhood of (x,y) to determine the value of g at (x,y) The va