CN105260986B - A kind of image magnification method of anti - Google Patents
A kind of image magnification method of anti Download PDFInfo
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- CN105260986B CN105260986B CN201510669656.7A CN201510669656A CN105260986B CN 105260986 B CN105260986 B CN 105260986B CN 201510669656 A CN201510669656 A CN 201510669656A CN 105260986 B CN105260986 B CN 105260986B
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- 238000000034 method Methods 0.000 title claims abstract description 43
- 230000003321 amplification Effects 0.000 claims abstract description 32
- 238000003199 nucleic acid amplification method Methods 0.000 claims abstract description 32
- 238000001914 filtration Methods 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 10
- 238000003707 image sharpening Methods 0.000 abstract description 4
- 238000011156 evaluation Methods 0.000 abstract 1
- 238000013461 design Methods 0.000 description 4
- 238000012544 monitoring process Methods 0.000 description 2
- 238000012952 Resampling Methods 0.000 description 1
- 230000001046 anti-mould Effects 0.000 description 1
- 239000002546 antimould Substances 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformations in the plane of the image
- G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
- G06T3/4084—Scaling of whole images or parts thereof, e.g. expanding or contracting in the transform domain, e.g. fast Fourier transform [FFT] domain scaling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/73—Deblurring; Sharpening
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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
- G06T2207/10016—Video; Image sequence
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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
- G06T2207/20172—Image enhancement details
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- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
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Abstract
The invention discloses a kind of image magnification methods of anti, the evaluation index of image fog-level is obtained according to the gradient of input picture, in conjunction with amplification factor, image resolution ratio, ask for image sharpening intensive parameter, under the control of the parameter, the sharpening being adapted with quality of input image grade is performed；And then the overall amplification process of image is decomposed into the amplification step by step of several small multiplying powers, and it is 1.2 times high using the common each enlarged drawing image width of interpolation method, until meeting overall magnification requirement.The present invention effectively eliminates the intrinsic blurring effect of interpolation method, significantly improves the clarity of enlarged drawing under the premise of unobvious increase computational complexity.
Description
Technical field
The invention belongs to technical field of image processing, are related to a kind of image interpolation amplification method, and in particular to a kind of anti-mould
The image magnification method of paste.
Technical background
Currently, digital camera or camera can easily shoot high-resolution video, but store or pass for saving
The considerations of defeated cost, still remains a large amount of low resolution in application scenarios such as video monitoring, video conference and Web TVs
Video content.Video amplifier technology thus be widely used in promoted image spatial resolution, to adapt to screen actual displayed
The requirement in region.Some special applications, such as video monitoring also relate to and image local area are amplified to distinguish
Know the detail content of image.
A large amount of video image zooming technology is developed, from the image super-resolution for being simply interpolated into complexity.Figure
As super-resolution technique although clearer amplification effect can be obtained, computational complexity remains high；It is more inconvenient that
Image super-resolution generally will precondition sample database, lack universality and operability in practical applications.Therefore, at present
Video Applications software or media player in the image magnification technology that uses mainly based on simple, quick interpolation method,
Such as bilinear interpolation (Bilinear), bi-cubic interpolation (Bicubic), blue hereby interpolation (Lanczos) etc..However, these are passed through
The interpolation amplification method of allusion quotation simultaneously there is also one it is serious the defects of, when original image clarity is not high (such as through overcompression
Image) or amplification factor it is excessive when, there are apparent blurring effects for the image after interpolation amplification, influence the subjective body of viewer
It tests.
Space sharpening filter is a kind of high-pass filter, can strengthen the high fdrequency component of digital picture, just and interpolation
The low-pass effect of filtering is opposite.Therefore, if assigning a sharpening operation before image interpolation amplification, it would be possible to certain journey
The blurring effect that interpolation is brought is offset on degree.In addition, image single amplification multiple it is more high easily cause it is fuzzy, on the contrary, amplification
Multiple is lower fuzzy lighter.Therefore, it if high magnification numbe amplification process to be converted to the amplification step by step of several low power numbers, will help
In the promotion of enlarged drawing quality.Based on the two angles, the present invention proposes a kind of image magnification method of anti.
Invention content
In order to solve the above-mentioned technical problem, image sharpening operation and interpolation arithmetic are cascaded into an entirety by the present invention, are led to
It crosses and sharpens the profile details that image is strengthened in pretreatment, it is slotting so as to eliminate for the signal source that interpolation arithmetic contribution radio-frequency component is promoted
The low pass blurring effect of value process influences.
The technical solution adopted in the present invention is：A kind of image magnification method of anti, which is characterized in that including following
Step：
Step 1：Image fog-level, the fuzziness index of calculating input image are weighed using the Gradient Features of image；
Step 2：Three elements of fuzziness index, overall magnification, image resolution ratio of comprehensive input picture, calculate defeated
Enter the sharpening intensities parameter of image；
Step 3：According to the sharpening intensities constant being calculated, sharpening algorithm is called to be sharpened input picture；
Step 4：The amplifieroperation that overall magnification is R is decomposed into the amplifications step by step of several small multiplying powers, determine by
The execution frequency n of grade amplification；
Step 5：Perform n times interpolation amplification operation step by step.
Preferably, the calculating of the fuzziness index described in step 1, with the following method：
Fuzziness index Fb is defined as to the gradient of energy normalized：
Wherein, Gx, Gy represent horizontal and vertical gradient figure respectively, pass through Sobel, Roberts or Prewitt gradient operator
It asks for；E is the energy of input picture, is calculated with input image pixels I" .* " representing matrix dot-product operation.
Preferably, the calculating of the sharpening intensities parameter lambda described in step 2, with the following method：
Wherein, Fb is fuzziness index, and R is overall magnification, and S is the area of input picture, multiplies height, S equal to lengthcif
For a constant on the basis of CIF format-pattern areas.
Preferably, the sharpening operation described in step 3, is to fold the high-pass filtered version of input picture after adjustment
It is added on original input picture, calculation formula is：
Y (n, m)=x (n, m)+λ z (n, m)；
Here, x, y represent the input picture after original input picture and sharpening respectively, and z represents the input after high-pass filtering
Image is obtained with two-dimentional Laplacian differential operators, and λ is sharpening intensities parameter.
Preferably, the determining execution frequency n amplified step by step described in step 4, using following rule：
Every time by the width of image and 1.2 times highly enlarged, if overall magnification is R, every time 1.2 times of amplification, by
The secondary execution frequency n for being amplified to R times is calculated asSymbolRepresent lower rounding.
Preferably, the execution n times interpolation amplification operation step by step described in step 5, interpolation method include but not limited to
Bilinear interpolation, bi-cubic interpolation method, blue hereby interpolation method.
Compared with existing image magnification scheme, the present invention has the advantages that：
(1) present invention applies a sharpening pretreatment before simple interpolation method, and increasing operation in unobvious answers
Under the premise of miscellaneous degree, the intrinsic blurring effect of interpolation method is effectively eliminated, significantly improves the clarity of enlarged drawing；
(2) compared with complicated super-resolution scheme, the method for the present invention does not involve sample image training, has and realizes letter
The advantages of list, treatment effeciency is high, and universality is strong；
(3) the method for the present invention based on sharpening algorithm and interpolation algorithm be all from mature technology, convenient for assembling existing mould
Block integration realization.
Description of the drawings
Fig. 1：The process chart of the embodiment of the present invention；
Fig. 2：The amplification effect comparative examples figure of the embodiment of the present invention, wherein (a) is put for original image through common bilinearity
Big 4 times of design sketch, (b) are that method of the original image through the present invention amplifies 4 times of design sketch, and (c) is decoding image through common two-wire
Property amplification 4 times of design sketch, (d) be decode image through the present invention method amplification 4 times of design sketch.
Specific embodiment
Understand for the ease of those of ordinary skill in the art and implement the present invention, with reference to the accompanying drawings and embodiments to this hair
It is bright to be described in further detail, it should be understood that implementation example described herein is merely to illustrate and explain the present invention, not
For limiting the present invention.
Image sharpening operation and interpolation arithmetic are cascaded into one by a kind of image magnification method of anti provided by the invention
A entirety.The intensity of sharpening operation should be adapted with input picture sole mass, and clearly image answers weak sharpening, fuzzy image
It should sharpen by force, otherwise, sharpen the pretreatment image that radio-frequency component enhancing cannot be not only provided for subsequent interpolation operation, instead can
It damages image, reduce eye fidelity.Lead to blurred image factor from many aspects, such as target and the opposite fortune of video camera
The motion blur of movable property life, camera lens defocus, atmospheric scattering, filterings such as compression of images, noise reduction and resampling, etc..These
Process causes image detail to lose and weakens object edge profile gradients bar none, therefore, using the Gradient Features of image
The fog-level of picture engraving, the gradient map of clear image is sharp keen, and the gradient map of blurred picture is flat.
Image Sharpening Algorithm usually with a sharpening intensities state modulator sharpness, applies much degree to input picture
Sharpening be at least considered as three factors.The first and most importantly fog-level of image can use fuzziness index to weigh
Amount；Secondly the amplification factor of image, the amplification factor the high more easily causes Low-pass interpolation blurring effect, should correspondingly perform
The stronger blurring effect sharpened to compensate amplification；It is finally the resolution ratio of input picture itself, the lower image of resolution ratio
Details conservation degree is poorer, more needs to enhance details by sharpening.
Based on considerations above, the complete process flow of the method for the present invention is as shown in Figure 1, comprise the steps of：
Step 1：Image fog-level, the fuzziness index of calculating input image are weighed using the Gradient Features of image；
Fuzziness index Fb is defined as the gradient of energy normalized by the influence of integrating image intensity
Here, Gx, Gy represent horizontal and vertical gradient figure respectively, can pass through the classics such as Sobel, Roberts, Prewitt
Gradient operator ask for, E be input picture energy, with input image pixels I calculate" .* " representing matrix dot product
Operation.
Step 2：Three elements of fuzziness index, overall magnification, image resolution ratio of comprehensive input picture, calculate defeated
Enter the sharpening intensities parameter of image；
Incorporate experience into data, the calculating of sharpening intensities parameter lambda, with the following method：
Wherein, Fb is fuzziness index, and R is overall magnification, and S is the area of input picture, multiplies height, S equal to lengthcif
For a constant on the basis of CIF format-patterns area (352x288).
Step 3：According to the sharpening intensities constant being calculated, sharpening algorithm is called to be sharpened input picture；
The sharpening algorithm that the present invention uses is, the high-pass filtered version of input picture image is added to original after adjustment
On beginning input picture image, calculation formula is
Y (n, m)=x (n, m)+λ z (n, m)；
Here, x, y represent the input picture image after original input picture image and sharpening respectively, and z represents high-pass filtering
Input picture image afterwards is obtained with two-dimentional Laplacian differential operators, and λ is sharpening intensities parameter.
Step 4：The amplifieroperation that overall magnification is R is decomposed into the amplifications step by step of several small multiplying powers, determine by
The execution frequency n of grade amplification；
Every time by the width of image and 1.2 times highly enlarged, if overall magnification is R, every time 1.2 times of amplification, by
The secondary execution frequency n for being amplified to R times is calculated asSymbolRepresent lower rounding.
Step 5：Perform n times interpolation arithmetic step by step, alternative interpolation method includes but not limited to bilinear interpolation, double
Cube interpolation, blue hereby interpolation.
It should be understood that the part that this specification does not elaborate belongs to the prior art.
It should be understood that the above-mentioned description for preferred embodiment is more detailed, can not therefore be considered to this
The limitation of invention patent protection range, those of ordinary skill in the art are not departing from power of the present invention under the enlightenment of the present invention
Profit is required under protected ambit, can also be made replacement or deformation, be each fallen within protection scope of the present invention, this hair
It is bright range is claimed to be determined by the appended claims.
Claims (5)
1. a kind of image magnification method of anti, which is characterized in that include the following steps：
Step 1：Image fog-level, the fuzziness index of calculating input image are weighed using the Gradient Features of image；
The calculating of the fuzziness index, with the following method：
Fuzziness index Fb is defined as to the gradient of energy normalized：
Wherein, Gx, Gy represent horizontal and vertical gradient figure respectively, are asked by Sobel, Roberts or Prewitt gradient operator
It takes；E is the energy of input picture, is calculated with input image pixels I" .* " representing matrix dot-product operation；
Step 2：Three elements of fuzziness index, overall magnification, image resolution ratio of comprehensive input picture calculate input figure
The sharpening intensities parameter lambda of picture；
Step 3：According to the sharpening intensities parameter lambda being calculated, sharpening algorithm is called to be sharpened input picture；
Step 4：The amplifieroperation that overall magnification is R is decomposed into the amplification step by step of several small multiplying powers, determines to put step by step
Big execution frequency n；
Step 5：Perform n times interpolation amplification operation step by step.
2. the image magnification method of anti according to claim 1, it is characterised in that：Sharpening described in step 2 is strong
The calculating of parameter lambda is spent, with the following method：
Wherein, Fb is fuzziness index, and R is overall magnification, and S is the area of input picture, multiplies height, S equal to lengthcifIt is one
Constant on the basis of CIF format-pattern areas.
3. the image magnification method of anti according to claim 1, it is characterised in that：Calling described in step 3 is sharp
Change algorithm to be sharpened input picture, be that the high-pass filtered version of input picture is added to be originally inputted figure after adjustment
As upper, calculation formula is：
Y (n, m)=x (n, m)+λ z (n, m)；
Here, x, y represent the input picture after original input picture and sharpening respectively, and z represents the input picture after high-pass filtering,
It is obtained with two-dimentional Laplacian differential operators, λ is sharpening intensities parameter.
4. the image magnification method of anti according to claim 1, it is characterised in that：Described in step 4 determine by
The execution frequency n of grade amplification, using following rule：
Every time by the width of image and 1.2 times highly enlarged, if overall magnification is R, every time 1.2 times of amplification, gradually put
Big to R times of execution frequency n is calculated asSymbolRepresent lower rounding.
5. the image magnification method of anti according to claim 1, it is characterised in that：Execution n times described in step 5
Interpolation amplification operation step by step, interpolation method include but not limited to bilinear interpolation, bi-cubic interpolation method, blue hereby interpolation method.
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| CN106709873B (en) * | 2016-11-11 | 2020-12-18 | 浙江师范大学 | Super-resolution method based on cubic spline interpolation and iterative updating |
| CN106875350A (en) * | 2017-01-05 | 2017-06-20 | 宇龙计算机通信科技(深圳)有限公司 | Method, device and the terminal of sharpening treatment are carried out to blurred picture |
| CN111737665A (en) * | 2019-04-27 | 2020-10-02 | 常英梅 | Grading display method for handheld mobile terminal |
| CN110446071A (en) * | 2019-08-13 | 2019-11-12 | 腾讯科技（深圳）有限公司 | Multi-media processing method, device, equipment and medium neural network based |
| CN111028182B (en) * | 2019-12-24 | 2024-04-26 | 北京金山云网络技术有限公司 | Image sharpening method, device, electronic equipment and computer readable storage medium |
| CN111245816B (en) * | 2020-01-08 | 2021-01-26 | 山东挚友企服信息科技有限公司 | Data uploading system and method based on content detection |
| CN111654627B (en) * | 2020-06-09 | 2021-11-26 | 展讯通信（上海）有限公司 | Digital zooming method, device, equipment and storage medium |
| CN112037135B (en) * | 2020-09-11 | 2023-06-09 | 上海瞳观智能科技有限公司 | Method for magnifying and displaying selected image key main body |
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Effective date of registration: 20200930 Address after: 519000 unit 1, No. 33, Haihe street, Hengqin New District, Zhuhai City, Guangdong Province Patentee after: ZHUHAI DAHENGQIN TECHNOLOGY DEVELOPMENT Co.,Ltd. Address before: 430072 Hubei Province, Wuhan city Wuchang District of Wuhan University Luojiashan Patentee before: WUHAN University |