DIGITAL IMAGE PROCESSING (R18A0422)
Walsh transforms Hadamard Transform
Chapter3 Image Transforms
3.2 The Fourier Transform and Properties h bl f. • 3.3 Other Separable Image Transforms. • 3.4 Hotelling Transform. Digital Image Processing.
LECTURE NOTES ON DIGITAL IMAGE PROCESSING
For our purposes the process of sampling a 1-D signal can be reduced to three facts and a theorem. •. Fact 1: The Fourier Transform of a discrete-time signal
Distance Transformations in Digital Images
Consider a digital binary image consisting of feature and non-feature pixels. The features can be points
Lecture 2: Geometric Image Transformations
8 sept. 2005 A spatial transformation of an image is a geometric transformation of the ... A digital image array has an implicit grid.
Fundamentals of Digital Image Processing
Digital Image Processing: Problems and Applications 1. Image Representation and Modeling 4 The One-Dimensional Discrete Fourier Transform (DFT) 141.
Compression Restoration
“Compressive Sensing
CHAPTER 2 DIGITAL IMAGE TRANSFORM ALGORITHMS
I. Pitas Digital Image Processing Fundamentals. Digital Image Transform Algorithms. THESSALONIKI 1998. 2.2. Contents. ?Introduction.
The Haar–Wavelet Transform in Digital Image Processing: Its Status
The digital images may be treated as such ”spiky” signals. Unfortunately the Haar Transform has poor energy compaction for image
Need for transform 2D Orthogonal and Unitary transform and its
For most image processing applications anyone of the mathematical transformation are applied to the signal or images to obtain further information from that
LECTURE NOTES
ONDIGITAL IMAGE PROCESSING
PREPARED BY
DR. PRASHANTA KUMAR PATRA
COLLEGE OF ENGINEERING AND TECHNOLOGY, BHUBANESWAR 2Digital Image Processing
UNIT-I
DIGITAL IMAGE FUNDAMENTALS AND TRANSFORMS
1. ELEMENTS OF VISUAL PERCEPTION
1.1 ELEMENTS OF HUMAN VISUAL SYSTEMS
ͻ The following figure shows the anatomy of the human eye in cross section ͻ There are two types of receptors in the retinaThe rods are long slender receptors
The cones are generally shorter and thicker in structure ͻ The rods and cones are not distributed evenly around the retina.ͻ Rods and cones operate differently
Rods are more sensitive to light than cones.
At low levels of illumination the rods provide a visual response called scotopic vision Cones respond to higher levels of illumination; their response is called photopic vision 3Rods are more sensitive to light than the cones.
Digital Image Processing
ͻ There are three basic types of cones in the retina ͻ These cones have different absorption characteristics as a function of wavelength with peak absorptions in the red, green, and blue regions of the optical spectrum.ͻ is blue, b is green, and g is red
Most of the cones are at the fovea. Rods are spread just about everywhere except the fovea There is a relatively low sensitivity to blue light. There is a lot of overlap 4Digital Image Processing
51.2 IMAGE FORMATION IN THE EYE
6Digital Image Processing
71.3 CONTRAST SENSITIVITY
ͻ The response of the eye to changes in the intensity of illumination is nonlinear ͻ Consider a patch of light of intensity i+dI surrounded by a background intensity I as shown in the following figure ͻ Over a wide range of intensities, it is found that the ratio dI/I, called the Weber fraction, is nearly constant at a value of about 0.02. ͻ This does not hold at very low or very high intensities ͻ Furthermore, contrast sensitivity is dependent on the intensity of the surround.Consider the second panel of the previous figure.
81.4 LOGARITHMIC RESPONSE OF CONES AND RODS
ͻ The response of the cones and rods to light is nonlinear. In fact many image processing systems assume that the eye's response is logarithmic instead of linear with respect to intensity. ͻ To test the hypothesis that the response of the cones and rods are logarithmic, we examine the following two cases: ͻ If the intensity response of the receptors to intensity is linear, then the derivative of the response with respect to intensity should be a constant. This is not the case as seen in the next figure. ͻ To show that the response to intensity is logarithmic, we take the logarithm of the intensity response and then take the derivative with respect to intensity. This derivative is nearly a constant proving that intensity response of cones and rods can be modeled as a logarithmic response. ͻ Another way to see this is the following, note that the differential of the logarithm of intensity is d(log(I)) = dI/I. Figure 2.3-1 shows the plot of dI/I for the intensity response of the human visual system. ͻ Since this plot is nearly constant in the middle frequencies, we again conclude that the intensity response of cones and rods can be modeled as a logarithmic response. 9Digital Image Processing
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