Image Enhancement 1. Introduction
• The principal objective of image enhancement is to
process a given image so that the result is more
• Introduction
suitable than the original image for a specific
• Enhancement by point processing application.
• Simple intensity transformation
• It accentuates or sharpens image features such as
• Histogram processing edges, boundaries, or contrast to make a graphic
• Spatial filtering display more helpful for display and analysis.
• Smoothing filters • The enhancement doesn't increase the inherent
• Sharpening filters information content of the data, but it increases the
• Enhancement in the frequency domain dynamic range of the chosen features so that they
can be detected easily.
• Pseudo-color image processing
Image Enhancement
Point operation Spatial operation Transform operation Pseudocoloring
y contrast stretching y Noise smoothing y Linear filtering y False coloring
y Noise clipping y Median filtering y Root filtering y Pseudocoloring
y Window slicing y LP, HP & BP filtering y Homomorphic filtering
y Histogram modeling y Zooming
,• The greatest difficulty in image enhancement is Frequency domain enhancement methods:
quantifying the criterion for enhancement and,
• These methods enhance an image f(x,y) by
therefore, a large number of image enhancement
convoluting the image with a linear, position
techniques are empirical and require interactive
invariant operator.
procedures to obtain satisfactory results.
• Image enhancement methods can be based on either • The 2D convolution is performed in frequency
spatial or frequency domain techniques. domain with DFT.
Spatial domain: g(x,y)=f(x,y)*h(x,y)
Spatial domain enhancement methods: Frequency domain: G(w1,w2)=F(w1,w2)H(w1,w2)
• Spatial domain techniques are performed to the
image plane itself and they are based on direct
manipulation of pixels in an image.
• The operation can be formulated as g(x,y) =
T[f(x,y)], where g is the output, f is the input image
and T is an operation on f defined over some
neighborhood of (x,y).
• According to the operations on the image pixels, it
can be further divided into 2 categories: Point
operations and spatial operations (including linear
and non-linear operations).
, 2. Enhancement by point processing
• These processing methods are based only on the
intensity of single pixels.
2.1 Simple intensity transformation:
(a). Image negatives:
• Negatives of digital images are useful in numerous
applications, such as displaying medical images and
photographing a screen with monochrome positive Original
film with the idea of using the resulting negatives as
normal slides.
• Transform function T : g(x,y)=L-f(x,y), where L is
the max. intensity.
Negative
• The principal objective of image enhancement is to
process a given image so that the result is more
• Introduction
suitable than the original image for a specific
• Enhancement by point processing application.
• Simple intensity transformation
• It accentuates or sharpens image features such as
• Histogram processing edges, boundaries, or contrast to make a graphic
• Spatial filtering display more helpful for display and analysis.
• Smoothing filters • The enhancement doesn't increase the inherent
• Sharpening filters information content of the data, but it increases the
• Enhancement in the frequency domain dynamic range of the chosen features so that they
can be detected easily.
• Pseudo-color image processing
Image Enhancement
Point operation Spatial operation Transform operation Pseudocoloring
y contrast stretching y Noise smoothing y Linear filtering y False coloring
y Noise clipping y Median filtering y Root filtering y Pseudocoloring
y Window slicing y LP, HP & BP filtering y Homomorphic filtering
y Histogram modeling y Zooming
,• The greatest difficulty in image enhancement is Frequency domain enhancement methods:
quantifying the criterion for enhancement and,
• These methods enhance an image f(x,y) by
therefore, a large number of image enhancement
convoluting the image with a linear, position
techniques are empirical and require interactive
invariant operator.
procedures to obtain satisfactory results.
• Image enhancement methods can be based on either • The 2D convolution is performed in frequency
spatial or frequency domain techniques. domain with DFT.
Spatial domain: g(x,y)=f(x,y)*h(x,y)
Spatial domain enhancement methods: Frequency domain: G(w1,w2)=F(w1,w2)H(w1,w2)
• Spatial domain techniques are performed to the
image plane itself and they are based on direct
manipulation of pixels in an image.
• The operation can be formulated as g(x,y) =
T[f(x,y)], where g is the output, f is the input image
and T is an operation on f defined over some
neighborhood of (x,y).
• According to the operations on the image pixels, it
can be further divided into 2 categories: Point
operations and spatial operations (including linear
and non-linear operations).
, 2. Enhancement by point processing
• These processing methods are based only on the
intensity of single pixels.
2.1 Simple intensity transformation:
(a). Image negatives:
• Negatives of digital images are useful in numerous
applications, such as displaying medical images and
photographing a screen with monochrome positive Original
film with the idea of using the resulting negatives as
normal slides.
• Transform function T : g(x,y)=L-f(x,y), where L is
the max. intensity.
Negative
