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Lecture 3 (2.5.07) Image Enhancement in Spatial Domain. Shahram Ebadollahi. DIP ELEN E4830. Today’s Lecture - Outline. Review of Lecture 2 Processing Images in Spatial Domain: Intro Image Histogram Point Operations Using Histogram for Image Enhancement Kernel Operations.

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today s lecture outline
Today’s Lecture - Outline
  • Review of Lecture 2
  • Processing Images in Spatial Domain: Intro
  • Image Histogram
  • Point Operations
  • Using Histogram for Image Enhancement
  • Kernel Operations
today s lecture outline3
Today’s Lecture - Outline
  • Review of Lecture 2
  • Processing Images in Spatial Domain: Intro
  • Image Histogram
  • Point Operations
  • Using Histogram for Image Enhancement
  • Kernel Operations
today s lecture outline4
Today’s Lecture - Outline
  • Review of Lecture 2
  • Processing Images in Spatial Domain: Intro
  • Image Histogram
  • Point Operations
  • Using Histogram for Image Enhancement
  • Kernel Operations
processing images in spatial domain introduction
Processing Images in Spatial Domain: Introduction

: Spatial operator defined on a neighborhood N of a given pixel

point processing

mask processing

today s lecture outline7
Today’s Lecture - Outline
  • Review of Lecture 2
  • Processing Images in Spatial Domain: Intro
  • Image Histogram
  • Point Operations
  • Using Histogram for Image Enhancement
  • Kernel Operations
image histogram

H

H

Image Histogram

normalized

histogram

0.5

bi-level image

0

255

256x256

Pixel values linearly increasing from 0 to 255 with increasing column index

histogram

1/256

0

255

256x256

today s lecture outline10
Today’s Lecture - Outline
  • Review of Lecture 2
  • Processing Images in Spatial Domain: Intro
  • Image Histogram
  • Point Operations
  • Using Histogram for Image Enhancement
  • Kernel Operations
point processing thresholding
Point Processing:Thresholding

Input gray-level value

Output gray-level value

point processing bit plane slicing example
Point Processing:Bit-plane Slicing (example)

Point operation for obtaining n-th bit-plane:

Bi-level image

n=7

n=6

n=4

n=5

today s lecture outline18
Today’s Lecture - Outline
  • Review of Lecture 2
  • Processing Images in Spatial Domain: Intro
  • Image Histogram
  • Point Operations
  • Using Histogram for Image Enhancement
  • Kernel Operations
histogram modification
Apply a transform to an image such that the resulting image has desired histogram.

Histogram Equalization (linearization)

Histogram Specification (matching)

Non-adaptive vs. Adaptive Histogram Modification

Global histogram

Local histogram

Histogram Modification
histogram equalization
Histogram Equalization

Equalized Image

Source image

Corresponding Histograms

histogram equalization21
Often images poorly use the full range of the gray scale

Solution:

Transform image such that its histogram is spread out more evenly in gray scale

Rather than guessing the parameters and the form of the transformation use original gray-scale distribution as the cue

Histogram Equalization
histogram equalization22
Histogram Equalization

Histogram Equalization

# pixels with the j-th gray-level

Point operation for equalizing histogram for the example image

image size

adaptive histogram equalization
Adaptive Histogram Equalization

y

(0,0)

Histogram Equalization

Note: local structure revealed

x

today s lecture outline26
Today’s Lecture - Outline
  • Review of Lecture 2
  • Processing Images in Spatial Domain: Intro
  • Image Histogram
  • Point Operations
  • Using Histogram for Image Enhancement
  • Kernel Operations
kernel operator intro
Kernel Operator: Intro

Note: need to handle borders of the image

kernel operator intro28
Kernel Operator: Intro

Spatial Filtering

kernel

smoothing image averaging

Low-pass filter

FT

FT

Smoothing: Image Averaging

*

Image edges are softened

smoothing averaging example
Smoothing: Averaging (example)

original

3x3

5x5

9x9

Noise effect is gone, but image edges are heavily blurred also

15x15

35x35

image sharpening 1 st derivative
Image Sharpening: 1-st derivative

Image gradient:

Robert’s operator

Sobel filter in frequency domain

Sobel’s operator

image sharpening 2 nd derivative35
Image Sharpening: 2-nd derivative

+

*

Laplacian filter in frequency domain

high boost filtering
High-boost Filtering

Avg.

-

+

+

Unsharp mask:

high-boost with A=1

recap
Recap
  • Point operations vs. Kernel Operations
  • Image Histogram
  • Image Enhancement using Point Operators
    • Contrast Stretching
    • Gamma Correction
  • Using Image Histogram for Enhancement
    • Histogram Equalization
    • Histogram Matching
  • Image Enhancement using Kernel Operators
    • Low-pass filtering (averaging)
    • High-pass filtering (sharpening)