CN108629754A - ISAR image self-adaptive detail enhancement method - Google Patents
ISAR image self-adaptive detail enhancement method Download PDFInfo
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- 238000012545 processing Methods 0.000 claims description 11
- 230000003044 adaptive effect Effects 0.000 claims description 7
- 238000002474 experimental method Methods 0.000 description 7
- 238000013459 approach Methods 0.000 description 3
- 238000003384 imaging method Methods 0.000 description 2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/40—Image enhancement or restoration using histogram techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/90—Dynamic range modification of images or parts thereof
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
- G06T2207/10044—Radar image
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20004—Adaptive image processing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
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- G06T2207/20172—Image enhancement details
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Abstract
The invention provides an ISAR image self-adaptive detail enhancement method which comprises the following steps of S1 obtaining L-level gray level histogram of an ISAR image, S2 compressing empty amplitude levels, S3 judging conditions, S4 compressing low-probability amplitude levels, S5 expanding high-probability amplitude levels, and S6 mapping gray level.
Description
Technical field
At ISAR (Inverse Synthetic Aperture Radar, Inverse Synthetic Aperture Radar) image
Reason technology more particularly to a kind of ISAR image adaptive details based on the compression of redundancy amplitude level and the extension of high probability amplitude level increase
Strong method.
Background technology
It is different from optical imagery, ISAR images in imaging process by target property, radar system, ambient noise and at
As the influence of many factors such as algorithm, it is usually expressed as isolated scattering center distribution, has sparsity, dynamic range big, right
The features such as more relatively low than degree.This makes subsequent image analysis, interpretation face prodigious difficulty, therefore before subsequent processing,
Details enhancing processing is carried out to ISAR images to seem very necessary.
Existing image detail enhancement method is mainly used in optical imagery.Such as Gamma bearing calibrations, referring to document (Peng
State's good fortune, the research and realization [J] Electronics Engineers that Gamma is corrected in the just great image procossings of woods, 2006, (2):30-32,
36.).This method improves picture contrast by a specific Gamma transformation operator.But this method is applied to tool
When the ISAR image details for having sparsity, dynamic range big enhance, it is difficult to obtain satisfactory effect.Such as histogram equalization
Method, referring to document, (marine sites Zhang Rui, Jia Na image enchancing method is summarized [J] liquid crystal and is shown, 2017, (10):828-834.),
Then increase dynamic range of images by making the probability density function of image gray levels meet the form of approaches uniformity distribution and carries
Hi-vision contrast, for dynamic range prodigious ISAR images, it is difficult to realize that details enhances, and to target subject
Enhancing can even weaken the visuality of image.Currently, not yet finding the related data in relation to ISAR image detail enhancement methods.
Invention content
In view of the above technology the problem of, the present invention propose a kind of based on the compression of redundancy amplitude level and high probability amplitude
The ISAR image adaptive detail enhancing methods of grade extension.This method utilizes the sparsity of ISAR images, to ISAR gray level images
Redundancy amplitude level is compressed, and is extended to high probability amplitude level, is realized the enhancing of image adaptive details, be can effectively improve
The visual effect of target ISAR images enriches target detail information.
The technical solution adopted by the present invention is：A kind of ISAR based on the compression of redundancy amplitude level and the extension of high probability amplitude level
Image adaptive detail enhancing method, this approach includes the following steps：
If the ISAR images G obtained the gray value g (m, n), wherein m ∈ [1, M], n ∈ [1, N] at coordinate (m, n), M
Indicated respectively with N ISAR images G orientation and distance to resolution cell number.
S1：Seek the L grade grey level histograms of ISAR images
It is L grades that the tonal range of ISAR images G, which is evenly dividing, obtains the corresponding L grades of grey level histogram of ISAR images G.
The value of amplitude series L is determined according to the tonal range size of ISAR images.
S2：Empty amplitude level compression
The amplitude level for being 0 comprising number of pixels in L grades of grey level histograms is deleted, the L ' grade gray scales that amplitude series is L ' are obtained
Histogram.
S3：Condition judgment
If L ' is less than the final series L of setting0, then S5 is executed；If L ' is more than the final series L of setting0, then execute
S4；Otherwise, S6 is executed.Final series L0The number of greyscale levels that quantifies as needed of value determine, generally take L0=256.
S4：Low probability amplitude level is compressed
Amplitude level minimum to the frequency of occurrences S4.1 carries out compression processing, i.e., by the amplitude level frequency of occurrences adjacent thereto
Lower amplitude level merges.
The new corresponding amplitude level grey level histogram of amplitude sum of series after S4.2 statistics compression processings, if new amplitude series
Equal to the final series L of setting0, then S6 is executed；Otherwise, S4.1 is executed.
S5：High probability amplitude level extends
S5.1 is extended processing to the highest amplitude level of the frequency of occurrences, i.e., the corresponding gray scale interval of the amplitude level is uniform
Division obtains two new amplitude levels.
New amplitude level grey level histogram after S5.2 statistics extension process, if new amplitude series is equal to the final level of setting
Number L0, then S6 is executed；Otherwise, S5.1 is executed.
S6：Grey scale mapping
L0The corresponding ISAR images G ' of grade grey level histogram, as enhanced result.
Beneficial effects of the present invention：Method processing through the invention is big for extraterrestrial target ISAR dynamic range of images
And the characteristics of high-amplitude area is sparse, histogram redundancy, adaptively carries out the compression of redundancy amplitude level and high probability amplitude level expands
Exhibition effectively improves the local contrast of target gray image in the case where retaining target detail information and not losing, improves target
The visual effect of ISAR images promotes target identification ability, has important engineering application value.
Description of the drawings
Fig. 1 is process flow of the present invention；
Fig. 2 is the grey level histogram for carrying out emulation experiment acquisition；
Fig. 3 is the result for carrying out contrast experiment.
Specific implementation mode
The specific implementation mode of the present invention is described further below in conjunction with the accompanying drawings.
Fig. 1 is process flow of the present invention.Fig. 2 is that the present invention is based on the compressions of redundancy amplitude level and the extension of high probability amplitude level
Image enhancement schematic diagram.
A kind of ISAR image adaptives based on the compression of redundancy amplitude level and the extension of high probability amplitude level of the present invention are thin
Enhancement Method is saved, this approach includes the following steps：
S1：Seek the L grade grey level histograms of ISAR images.
It is L grades that the tonal range of ISAR images G, which is evenly dividing, obtains the corresponding L grades of grey level histogram of ISAR images G.
The value of amplitude series L is determined according to the amplitude range size of ISAR images.Wherein, it is evenly dividing and is meant that so that gray scale is straight
The length in the corresponding tonal range section of each amplitude level of square figure is consistent.
S2：Empty amplitude level compression
The amplitude level for being 0 comprising number of pixels in L grades of grey level histograms is deleted, the intensity histogram that amplitude series is L ' is obtained
Figure.
S3：Condition judgment
By condition judgment, decision is to carry out the compression of low probability amplitude level and the extension of high probability amplitude level, or terminate skill
Art scheme.
S4：Low probability amplitude level is compressed
If carrying out amplitude level compression, the amplitude level minimum to the frequency of occurrences carries out compression processing, and the frequency of occurrences is minimum
Amplitude level refer to the amplitude level (i.e. gray scale interval) include the minimum amplitude level of number of pixels.The amplitude level is adjacent thereto
The lower amplitude level of the frequency of occurrences merges, and refers to from two (or 1) amplitude levels adjacent in gray scale interval with the amplitude level
In, selection is merged comprising the less amplitude level of pixel.The gray scale interval that new amplitude level includes is two width being merged
Spend the sum of the gray scale interval of grade.
S5：High probability amplitude level extends
Processing is extended to the highest amplitude level of the frequency of occurrences, i.e., is carried out the corresponding gray scale interval of the amplitude level uniform
Division obtains two new gray scale intervals, and each gray scale interval corresponds to a new amplitude level.Statistics falls into new amplitude level
Pixel number, to obtain new amplitude level grey level histogram.
S6：Grey scale mapping
Fig. 2 is the grey level histogram for carrying out emulation experiment acquisition.Fig. 2-(a) is that the ISAR images that use are corresponding in experiment
Original grey level histogram, amplitude series L=1024.When forming grey level histogram, 1024 are divided by Imhist functions
A rank.Fig. 2-(b) is using the present invention is based on the gray scale obtained after the compression of redundancy amplitude level and the extension of high probability amplitude level is straight
Fang Tu；Amplitude series includes L at this time0=256 ranks.It can be seen from the figure that by using the present invention, histogram is removing
It is expanded after redundancy, the local contrast of target gray image is effectively improved in the case where retaining target detail information and not losing
Degree.
Fig. 3 is the result for carrying out contrast experiment.(a) it is the original I SAR imaging results for emulating Aircraft Targets, is (b) to utilize
Gamma transformation (coefficient is 0.4) enhancing is as a result, (c) be the enhancing of existing histogram equalization as a result, (d) being to utilize the present invention
The image enhancement result that method obtains.It can be seen from the figure that Gamma converter techniques are to big with sparsity, dynamic range
When ISAR image enhancements, target and background noise is carried out at the same time enhancing.Histogram equalization method is very big for dynamic range
ISAR images for, it is difficult to realize details enhance, and to the enhancing of target subject even can thicken.And the side of the present invention
Method can more effectively enhance detailed information.
Further to verify effectiveness of the invention, System of Comprehensive Evaluation is built using three modules, is carried out
Quantitative analysis, including index of fuzziness, local contrast, details area variance.Index of fuzziness is smaller, local contrast is got over
Greatly, details area variance is bigger, and image enhancement performance is more superior.It is quantitative to the experimental result of Fig. 3 using above three index
Comparison, as shown in table 1.
1 quantitative comparison of table
It is as follows that comparing result is obtained according to table 1：
1. according to the comparing result of index of fuzziness and local contrast it is found that the method for the present invention is improving image detail letter
It is more excellent than other algorithms in terms of the abundant degree and local contrast of breath.
2. in terms of details area variance yields, the result of the method for the present invention is equally maximum, and increasing degree is larger.This result
Show the method for the present invention when realizing Larger Dynamic Ratage Coutpressioit, retain local detail or keeps the ability of local contrast than other
Method is more superior.
Emulation and contrast experiment are realized by Matlab2010a.Operating system is Microsoft Windows XP
ProfessionalSP3, processor are Pentium Dual-Core 2.7GHz.The measured data that Fig. 3 emulation experiments utilize is big
Small is 401 × 256, time overhead comparing result used, as shown in the table.
The time overhead (s) of 2. algorithms of different of table
| Algorithm | Gama is converted | Conventional histogram converts | This patent method |
| Time overhead (mean value) | 0.027 | 0.025 | 0.2 |
The comparing result provided from table can see, and the method for the present invention emulation hour operation quantity has certain increasing than other algorithms
It is long, but the increase of this time overhead can be ignored relative to the promotion of algorithm performance.
Claims (1)
1. a kind of ISAR image adaptives detail enhancing method, ISAR refer to Inverse Synthetic Aperture Radar, if the ISAR images G obtained
Gray value g (m, n) at coordinate (m, n), wherein m ∈ [1, M], n ∈ [1, N], M and N indicate ISAR images G in orientation respectively
To with distance to resolution cell number, which is characterized in that include the following steps：
S1：Seek the L grade grey level histograms of ISAR images：
It is L grades that the tonal range of ISAR images G, which is evenly dividing, obtains the corresponding L grades of grey level histogram of ISAR images G；Amplitude
The value of series L is determined according to the tonal range size of ISAR images；
S2：Empty amplitude level compression：
The amplitude level for being 0 comprising number of pixels in L grades of grey level histograms is deleted, the L ' grade intensity histograms that amplitude series is L ' are obtained
Figure；
S3：Condition judgment：
If L ' is less than the final series L of setting0, then S5 is executed；If L ' is more than the final series L of setting0, then S4 is executed；It is no
Then, S6 is executed；Final series L0The number of greyscale levels that quantifies as needed of value determine；
S4：Low probability amplitude level is compressed：
Amplitude level minimum to the frequency of occurrences S4.1 carries out compression processing, i.e., the amplitude level frequency of occurrences adjacent thereto is relatively low
Amplitude level merge；
The new corresponding amplitude level grey level histogram of amplitude sum of series after S4.2 statistics compression processings, if new amplitude series is equal to
The final series L of setting0, then S6 is executed；Otherwise, S4.1 is executed；
S5：High probability amplitude level extends：
S5.1 is extended processing to the highest amplitude level of the frequency of occurrences, i.e., is evenly dividing the corresponding gray scale interval of the amplitude level
Obtain two new amplitude levels；
New amplitude level grey level histogram after S5.2 statistics extension process, if new amplitude series is equal to the final series L of setting0,
Then execute S6；Otherwise, S5.1 is executed；
S6：Grey scale mapping：
L0The corresponding ISAR images G ' of grade grey level histogram, as enhanced result.
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