图像滤波和图像增强

Digital Negative
Digital negatives
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Intensity Level Slicing
Without background:
L a ≤ u ≤ b v= 0 otherwise
L a ≤ u ≤ b v= u otherwise
With background:
(3) Image Enhancement by Spatial (mask) Operation
Noise Smoothing Median Filtering Sharping Masking Zooming
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What Needs Enhancement?
Before launching into the methods of this chapter, it is useful to review some of the problems that need them. Two general categories of problems follow. An image needs improvement Low-level features must be detected
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Contrast Stretching
αu 0 ≤ u ≤ a v = β (u − a ) + va a ≤ u ≤ b γ (u − b ) + v b ≤ u ≤ L b
Where u is the gray-level values of input image 0-255, v is the transformed gray-level values of output image 0-255, L=255
gin
Input image
f(x)
gout=f(gin)
Output image
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Mapping function
Definition - A point operator applied to an image is an operator in which the output pixel is determined only by the input pixel, Out[x, y] = f (In[x, y]); possibly function f depends upon some global parameters. • A contrast stretching 对比度伸缩 operator is a point operator that uses a piecewise smooth function f (In[x, y]) of the input gray level to enhance important details of the image.
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Intensity Level Slicing
(a) Visual and infrared images
(b) Segment images
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Level slicing of intensity window[175,250]
Various Mapping Functions
gout gout gout
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Contrast Stretching
Cliping and thresholding
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Contrast Stretching
Cliping and thresholding
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Digital Negative
v = L−u
• Create digital negative of images • Display of medical images
Example 3: Image of airplane part has edges enhanced to support automatic recognition and measurement 4
This chapter deals mostly with traditional methods of image enhancement. Before moving on, it is important to define and distinguish the terms.
Scratches
Example 1: Scratches from original photo of San Juan are removed
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Example 2: Intensity of photo of Alaskan Pipeline rescaled to show much better detail
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Contrast Stretching
• Improve contrast due to poor /nonuniform lighting conditions or nonlinearity or small dynamic range of image sensor. • Typical contrast stretching transformation:
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Example
Left - Original sensed fingerprint; Center - Image enhanced by detection and thinning of ridges; Right - Identification of special features called minutia, which can be used for matching to millions of fingerprint representations in a database.
gin gout gout
gin gout
gin
gin
gin
gin
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Histogram Modification
When is it necessary to modify the Histogram of an image
The histogram of the “bad” image is very narrow (Fig. a), while the histogram of the “good” image is more spread (Fig. b). To change a “bad” image to the “good” image, we need modify the histogram.
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Image Enhancement
• Sharpening of image features ( such as edges or boundaries) to make a graphic display more useful for analysis. • Do not increase information content but increase dynamic range of chosen feature to be detected more easily.
• (2) Spatial (mask) operation
– – – – Noise smoothing Median filtering Sharping masking Zooming
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Points Operation
Two ways to enhance images: Changing the intensity values of pixels Transforming the pixels via a single function that maps an input gray value into a new output value Remapping the gray values is often called stretching 伸缩
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Contents
(1) What Needs Enhancement? (2) Image Enhancement by Point Operation
Contrast Stretching Digital Negative Intensity Level Slicing Histogram Modeling
• Thresholding: when a=b=t (threshold)
– Output becomes binary. – Useful for binary or other images that have bimodal distance of gray levels.
• Can define other similar operations.
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Intensity Level Slicing
Fully illuminates pixels lying in the interval [a,b] and removes the background. • Segmentation of certain gray level region • e.g. Image from remote sensing.
Chapter 3: Filtering & Enhancing Images
This chapter is about image processing, since the methods take an input image and create another image as output. Other appropriate terms often used are filtering, enhancement, or conditioning. The major motion is that the image contains some signal or structure, which we want to extract, along with uninteresting or unwanted variation, which we want to suppress. If decisions are made about the image, they are made at the level of a single pixel or its local neighborhood.
Image enhancement operators improve the detectability of important image details or objects by man or machine. Example operations include noise reduction (smoothing), contrast stretching, and sharpening (edge enhancement).
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Mapping function
255 gout f(x)
Output image
0
Input image
gin
255
Output image
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Contrast Stretching
(a) Original
(b) Enhanced
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Contrast Stretching
(a) Original
(b) Enhanced
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Contrast Stretching
• Example:
(a) Original
(b) Enhanced
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Contrast Stretching
• Clipping: when
α =γ =0
– Useful for noise reduction when input signal is known to lie in the range [a,b].
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Image Enhancement - Operations
(1) Point operations
– – – – Contrast stretching Noise clipping Window slicing Histogram modeling
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Image Enhancement -Operations
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Input image Mapping function (Reverse)
255 gout
f(x)=x -1
0
gin
255
Output image
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Digital Negative
Digital negatives
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Digital Negative
Digital negatives
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