spatial enhancement
TRANSCRIPT
SPATIAL FEATURE MANIPULATION
ABIN V. ARKKATTU
Image enhancement is the process of making images more useful.
The reasons for doing this include:
Highlighting interesting detail in images
Removing noise from images
Making images more visually appealing
Methods of Enhancement1. Contrast manipulation
Contrast stretching = expand the DN values beyond their natural range to fill the 0-255 range.
2. Spatial feature manipulationRefers to image texture.
Smooth areas have low spatial frequencies, gray values change gradually.
Rough areas have high spatial frequencies and gray values change abruptly.
Methods of Enhancement3. Multi-image manipulation.
Two or more images combined mathematically, commonly by ratios.
Used to develop green vegetative index images, e.g., the NDVI.
SPATIAL FEATURE MANIPULATION
SPATIAL FILTERING
CONVOLUTION
EDGE ENHANCEMENT
DIRECTIONAL FIRST DIFFRENCING
FOURIER ANALYSIS
Filters
Low-pass filter –
designed to emphasize larger, homogeneous areas of similar tone
and reduce smaller detail.
low-pass filters smooth the appearance of an image.
High-pass filters do the opposite –
sharpen the appearance of fine detail in an image.
Directional, or edge detection filters are designed to highlight
linear features, such as roads or field boundaries.
enhance features which are oriented in specific directions.
useful for detection of linear geologic structures.
Original image Low frequency component image
High frequency component image
Low Pass Filter
Image Frequencies
• Low Frequency Components = Slow Changes in Pixel Intensity
• regions of uniform intensity
High Frequency component of image
and filtering
•High Frequency Components = Rapid Changes in Pixel Intensity
•regions with lots of details
High Frequency Component
Convolution Spatial filtering is but one spatial application of the generic image processing operation called convolution. Convolving an image involves the following procedures.
•A moving window is established that contains an array of coefficients or weighing factors. Such arrays are referred to as operators or kernels , and they are normally an odd number of pixels in size (eg. 3 x 3,5 x 5)
•The kernel is moved throughout the original image and the DN at the center of the kernel in a second(convoluted) output image is obtained by multiplying each coefficient in the kernel by the corresponding DN in the original image and adding all the resulting products. This operation is performed for each pixel in the original image.
Convolution
The generic image processing operation
Spatial filter convolutionProcedure
Establish a moving window (operators/kernels)Moving the window throughout the original image
Example
(a) KernelSize: odd number of pixels (3x3, 5x5, 7x7, …)Can have different weighting scheme (Gaussian distribution, …)
(b) original image DN (c) convolved image DN
Pixels around border cannot be convolved
The purpose of edge enhancement is to highlight fine detail in an image or to restore, at least partially, detail that has been blurred (either in error or as a consequence of a particular method of image acquisition).
EDGE ENHANCEMENT
Edge enhancement
Typical procedures
Roughness kernel size
Rough smallSmooth large
Add back a fraction of gray level to the high frequency component image
High frequency exaggerate local contrast but lose low frequency brightness information
DIRECTIONAL FIRST DIFFRENCING
Determine the first derivative of gray levels with respect to agiven direction.
Normally add the display value median back to keep all positive values.
It is another enhancement technique aimed at emphasizing edges in image data.
It is a procedure that systematically compares each pixel in an image to one of its immediately adjacent neighbors and displays the difference in terms of the gray levels of an output image.
This process is mathematically asking to determine the first derivative of gray levels with respect to a given direction.
The direction used can be horizontal, vertical , or diagonal.
Fourier analysis
Spatial domain frequency domain
Fourier transform
Conceptual description
Fit a continuous function through the discrete DN values if they were plotted along each row and column in an imageThe “peaks and valleys” along any given row or column can be described mathematically by a combination of sine and cosine waves with various amplitudes, frequencies, and phases
Fourier spectrum
Low frequency centerHigh frequency outwardVertical aligned features horizontal componentsHorizontal aligned features vertical components
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