Chapter 3 Image Enhancement in the Spatial Domain
- Slides: 28
Chapter 3 Image Enhancement in the Spatial Domain
Outline n n n n Background Basic Gray-level transformation Histogram Processing Arithmetic-Logic Operation Basics of Spatial Filtering Smoothing Spatial Filters Sharpening Spatial Filters Combining Spatial Enhancement Methods
Background Image enhancement approaches fall into two broad categories: spatial domain methods and frequency domain methods. n The term spatial domain refers to the image plane itself. n g(x, y)= T[f(x, y)] , T is an operator on f, defined over some neighborhood of f(x, y) n
Size of Neighborhood Point processing n Larger neighborhood: mask (kernel, template, window) processing n
Gray-level Transformation Contrast stretching thresholding
Basic Gray Level Transformation n Image negatives: s =L-1 -r Log transformation: s =clog(1+r) Power-law transformation: s=crg
Image Negatives
Log Transformation
Gamma Correction (I) n Cathode ray tube (CRT) devices have an intensity-to-voltage response that is a power function, with exponents varying from 1. 8 to 2. 5.
Gamma Correction (II)
Piece-wise Linear Transformation n Contrast stretching Gray-level slicing (Figure 3. 11) Bit-plane slicing (Figures 3. 13 -14)
Gray-level Slicing
Bit-plane Slicing
Histogram Processing n n n The histogram of a digital image with gray -levels in the range [0, L-1] is a discrete function 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. Easy to compute, good for real-time image processing.
Four Basic Image Types
Histogram Equalization s= T(r) n What if we take the transformation T to be: n It can be shown that ps(s)=1 n Discrete version: n
Histogram Matching
Local Enhancements
Histogram Statistics n N-th moment of r about its mean:
Logic Operations
Arithmetic Operations Image Subtraction n Image Averaging n
Basics of Spatial Filtering • Mask, convolution kernels • Odd sizes
Smoothing Spatial Filters n Smoothing linear filters: averaging filters, low-pass filters n n n Box filter Weighted average Order-statistics filters: n n n Median-filter: removing salt-and-pepper noise Max filter Min filter
Sharpening Spatial Filters n Foundation:
The Laplacian n Development of the method:
Image Enhancement
The Gradient Simplification
Combining Spatial Enhancement Methods (a) original (b) Laplacian, (c) a+b, (d) Sobel of (a) (b) (c) (d)
- Image enhancement in spatial domain
- Image enhancement in spatial domain
- Image enhancement in spatial domain
- Image enhancement in spatial domain
- What is enhancement in the spatial domain?
- Image processing
- The objective of sharpening spatial filter is to
- Histogram equalization
- Combining spatial enhancement methods
- Combining spatial enhancement methods
- Spatial data vs non spatial data
- Image enhancement in night vision technology
- Objective of image enhancement
- Gamma correction image processing
- Image enhancement point processing techniques
- Contrast stretching
- Gonzalez
- Arithmetic
- Spatial domain
- Spatial operations in image processing
- In digital image processing
- Intensity transformation and spatial filtering
- Spatial and temporal redundancy in digital image processing
- Spatial resolution in digital image processing
- Introduction to functions (review game)
- Z domain to frequency domain
- Fourier series of trapezoidal waveform
- What is the z - transform of anu[n] and -anu[-n-1]
- Z domain to frequency domain