Image enhancement

Introduction to Photogrammetry and Remote Sensing (SGHG 1473)

Dr. Muhammad Zulkarnain Abdul Rahman

Image enhancement

• Enhancements are used to make it easier for visual interpretation and understanding of imagery

• Subtle differences in brightness value can be highlighted either by:

– Contrast modification or

– by assigning quite different colours to those levels (density slicing)

• Point operations change the value of each individual pixel independent of all other pixels

• Local operations change the value of individual pixels in the context of the values of neighboring pixels

Image enhancement

• Information enhancement includes:

– Image reduction,

– Image magnification,

– Transect extraction,

– Contrast adjustments (linear and non-linear),

– Band rationing,

– Spatial filtering,

– Fourier transformations,

– Principle components analysis,

– Image sharpening, and

– Texture transformations

Visualization

• Color spaces for visualization - Three approaches:

– Red-Green-Blue (RGB) space – based on additive principle of colors

• The way TV and computer screen operate

• 3 channel (R,G,B)

– Intensity-Hue-Saturation (IHS) space

– Yellow-Magenta-Cyan (YMC) space - based on subtractive principle of colors

Contrast enhancement

• Materials or objects reflect or emit similar amounts of radiant flux (so similar pixel value)

• Only intended to improve the visual quality of a displayed image by increasing the range (spreading or stretching) of data values to occupy the available image display range (usually 0-255)

• Linear technique

– Minimum-maximum contrast stretch

– Percentage linear contrast stretch

– Standard devia=on contrast stretch

– Piecewise linear contrast stretch

• Non-linear technique

– Histogram equaliza=on

Minimum-maximum contrast stretch

Contrast Stretching of Predawn

Thermal Infrared Data of the

the Savannah River

Original

Minimum-

maximum

+1 standard

deviation

Jensen, 2011

Piecewise linear contrast stretch

Characterised

by a set of user

specified break

points

Histogram equalization

• In practice a perfectly uniform histogram cannot be achieved for digital image data

• To make sure that each bar in the image histogram has the same height

• Such a histogram has associated with it an image that utilises the available brightness levels equally and

• Should give a display in which there is good representation of detail at all brightness values

• The method of producing a uniform histogram is known generally as histogram equalization

• Reduces the contrast in the very light or dark parts of the image associated with the tails of a normally distributed histogram

Jensen, 2011

Specific percentage

linear contrast stretch

designed to highlight the

thermal plume

Histogram Equalization

Contrast Stretching of Predawn Thermal

Infrared Data of the the Savannah River

Band ratioing

, ,

, ,

, ,

i j k

i j ratio

i j l

BVBV

BV=

, ,

, ,

, ,

i j k

i j ratio

i j l

BVBV

BV=

where:

BVi,j,k is the original input brightness value in band k

BVi,j,l is the original input brightness value in band l

BVi,j,ratio is the ratio output brightness value

Band

Ratioing of

Charleston,

SC Landsat

Thematic

Mapper

Data

Band Ratio Image

Landsat TM

Band 4 / Band 3

Spatial filtering

• Spatial Filtering to Enhance Low- and High-Frequency Detail and Edges

• A characteristics of remotely sensed images is a parameter called spatial frequency, defined as the number of changes in brightness value per unit distance for any particular part of an image

• Spatial frequency in remotely sensed imagery may be enhanced or subdued using two different approaches:

– Spatial convolution filtering based primarily on the use of convolution masks, and

– Fourier analysis which mathematically separates an image into its spatial frequency components

Spatial Convolution Filtering

• A linear spatial filter is a filter for which the brightness value (BVi,j,out) at location i,j in the output image is a function of some weighted average (linear combination) of brightness values located in a particular spatial pattern around the i,j location in the input image

• The process of evaluating the weighted neighboring pixel values is called two-dimensional convolution filtering.

Spatial Convolution Filtering

• The size of the neighborhood convolution mask

or kernel (n) is usually 3 x 3, 5 x 5, 7 x 7, 9 x 9, etc.

• We will constrain our discussion to 3 x 3

convolution masks with nine coefficients, ci,

defined at the following locations:

c1 c2 c3

Mask template = c4 c5 c6

c7 c8 c9

1 1 1

1 1 1

1 11

Spatial Convolution Filtering

• The coefficients, c1, in the mask are multiplied by the

following individual brightness values (BVi) in the

input image:

c1 x BV1 c2 x BV2 c3 x BV3

Mask template = c4 x BV4 c5 x BV5 c6 x BV6

c7 x BV7 c8 x BV8 c9 x BV9

The primary input pixel under investigation at any one time is BV5

= BVi,j

Spatial Convolution Filtering: Low

Frequency Filter

1

1

1

1

1

1

1

1

1

9

1

5,

1 2 3 9

int

...int

9

i i

iout

c BV

LFFn

BV BV BV BV

=

×

=

+ + + =

∑