Image Enhancement

The problem of image enhancement is usually considered a problem of quality improvement and image sharpening. This is a very important branch of image processing. Among the other processing algorithms, the algorithms that solve the problems of the image detail extraction against a complicated background are also part of the image enhancement. These algorithms may be very useful in different fields, such as medical and satellite imaging, graphic arts, etc.

The Frequency Correction
and
Image Sharpening

Global and high frequency correction are powerful spatial domain filters oriented towards the extraction of image details on the complex, obstructing or non-contrast background. These filters are the spatial approximations of the corresponding filters in the frequency domain. High frequency correction leads to extraction of the smallest image details and to a sharpening of the image. Global frequency correction leads to extraction (enhancement) of the details of various sizes.

Performing this type of operation directly in the frequency domain is a complicated computing problem. Current algorithms available on the market for local image enhancement do not provide the optimum solution for this problem. These algorithms are based on linear correcting filters (usually called “unsharp masking”). It is a well-known fact that linear filters are not as effective as nonlinear ones. In particular it means that linear frequency correction filters often move the global dynamic range of the image to the “white” or the “black” side. Thus, in extracting some details others are lost.

We propose strongly nonlinear algorithms for frequency correction. They are much more powerful and free of the disadvantages of linear filters. Nonlinear frequency correction may be accomplished by median-type filtering, or by Multi-Valued nonlinear filtering. This nonlinear approach is the preferred one, because it provides the most effective results, even when details being extracted have a very small amount of contrast. As a result, a global histogram of the image is by and large preserved; no extra “white” and “black” pixels appear in the resulting image. These filters also minimize shifts of the image boundaries.

The second solution is based on mixed spatial/frequency domain filtering. Image sharpening is achieved by the Median and Multi-valued Frequency Correction algorithms, and then the resulting image is corrected in frequency domain. This operation ensures preservation of input image boundaries, and eliminates possible shifts.

Examples

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Image Sharpening Examples

The original image “Sydney”	Histogram (Green Channel)
MVF-high frequency correction, 3x3 window, Red channel: G=6.5, Green: G=5.5, Blue: G=8.0	Histogram of the image to the left (Green Channel). This histogram does not contain any peaks
Traditional unsharp masking using high frequency correction (mean), 3x3 window, Red: G=0.8, Green: G=1.0, Blue: G=0.5. Many pixels became truly white or truly black.	Histogram of the image to the left (Green Channel). High peaks in “0” and “255” are clearly visible. It means that many pixels have become truly white or truly black. As a result, information in the corresponding pixels is lost

The original radar satellite image	Histogram
MVF-high frequency correction, 3x3 window, G=6.0	Histogram of the image to the left. The global histogram behavior is preserved: no new peaks and a number of white (255) pixels is even less than on the original image

The original X-ray medical image (tumor of lung)	MVF-global frequency correction, 35x35 window
Precise Edge Detection Upward The structure of the tumor is clearly visible	Precise Edge Detection Downward The structure of the tumor is clearly visible

The original X-ray medical image - mammogram.	MVF-high frequency correction, 3х3 window, G=8.0	Global frequency correction, 9x9 window, G1=0.5, G2=5.0, G3=0.5