Dr. Ghassabi [email protected] Tehran shomal University Spring 2015 [email protected]...
-
Upload
kathlyn-davidson -
Category
Documents
-
view
216 -
download
0
Transcript of Dr. Ghassabi [email protected] Tehran shomal University Spring 2015 [email protected]...
1
Tehran shomal UniversitySpring 2015
Digital Image ProcessingSession 3
2
Outline
• Introduction• Digital Image Fundamentals• Intensity Transformations and Spatial Filtering• Filtering in the Frequency Domain• Image Restoration and Reconstruction• Color Image Processing • Wavelets and Multi resolution Processing• Image Compression• Morphological Operation• Object representation• Object recognition
OutlineChapter 3
• Background• Some Basic Intensity Transformation Functions• Histogram Processing• Fundamentals of Spatial Filtering• Smoothing Spatial Filters• Sharpening Spatial Filters• Combining Spatial Enhancement Tools
Image Enhancement
• Methods– Spatial Domain:
• Linear• Nonlinear
– Frequency Domain:• Linear• Nonlinear
Transformation
• For 11 neighborhood: – Contrast Enhancement/Stretching/Point process
• For w w neighborhood:– Filtering/Mask/Kernel/Window/Template Processing
s T r
IE in Spatial Domain
Input gray level, r
Ou
tpu
t gr
ay le
vel,
s
Negative
Log
nth root
Identity
nth power
Inverse Log Some Basic Intensity Transformation Functions
Piecewise-Linear Transformation Functions
• Contrast Stretching• Contrast slicing• Bite-Plane slicing
Bit-plane SlicingHighlighting the contribution made to total image appearance by specific bitsSuppose each pixel is represented by 8 bitsHigher-order bits contain the majority of the visually significant dataUseful for analyzing the relative importance played by each bit of the image
Bit-plane Slicing
The (binary) image for bit-plane 7 can be obtained by processing the input image with a thresholding gray-level transformation.
Map all levels between 0 and 127 to 0Map all levels between 129 and 255 to 255
Bit-plane Slicing - Fractal Image
Bit-plane 7 Bit-plane 6
Bit-plane 5 Bit-plane 4 Bit-plane 3
Bit-plane 2 Bit-plane 1 Bit-plane 0
Histogram Processing
Enhancement based on statistical Properties: Local, Global
Histogram Definition
h(rk)=nk
Where rk is the kth gray level and nk is the number of pixels in the image having gray level rk
Normalized histogram:
P(rk)=nk/n
Histogram of an image represents the relative frequency of occurrence of various gray levels in the image