CN111968033A - Image scaling processing method and device - Google Patents
Image scaling processing method and device Download PDFInfo
- Publication number
- CN111968033A CN111968033A CN202010747284.6A CN202010747284A CN111968033A CN 111968033 A CN111968033 A CN 111968033A CN 202010747284 A CN202010747284 A CN 202010747284A CN 111968033 A CN111968033 A CN 111968033A
- Authority
- CN
- China
- Prior art keywords
- interpolation
- image
- scaling
- gray value
- weight coefficient
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000003672 processing method Methods 0.000 title claims abstract description 20
- 238000004364 calculation method Methods 0.000 claims abstract description 41
- 238000000034 method Methods 0.000 claims abstract description 24
- 230000003321 amplification Effects 0.000 abstract description 2
- 238000003199 nucleic acid amplification method Methods 0.000 abstract description 2
- 230000000694 effects Effects 0.000 description 3
- 230000014509 gene expression Effects 0.000 description 3
- 239000000654 additive Substances 0.000 description 1
- 230000000996 additive effect Effects 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- 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
- G06T3/4007—Scaling of whole images or parts thereof, e.g. expanding or contracting based on interpolation, e.g. bilinear interpolation
-
- 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
- G06T3/4023—Scaling of whole images or parts thereof, e.g. expanding or contracting based on decimating pixels or lines of pixels; based on inserting pixels or lines of pixels
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Image Processing (AREA)
- Editing Of Facsimile Originals (AREA)
Abstract
The invention discloses an image zooming processing method and device, wherein the method comprises the following steps: respectively obtaining interpolation positions of interpolation points of the target image in the horizontal direction and the vertical direction in the original image; determining all relevant pixel points corresponding to the interpolation positions in the original image; calculating the distance between the related pixel point and the interpolation position, and performing integer processing on the distance when the scaling is non-integer; calculating the weight coefficient of the related pixel points according to the distance after the integer processing; and calculating the gray value at the interpolation position according to the weight coefficient and the gray value of the related pixel point, and taking the gray value as the gray value of the interpolation point of the target image at the interpolation position. The image zooming processing method of the invention carries out integer processing on the distance; the calculated amount is effectively reduced, the numerical value is subjected to equal-scale amplification and reduction, no error is introduced, and the calculation precision is higher.
Description
Technical Field
The invention belongs to the technical field of image processing, and particularly relates to an image scaling processing method.
Background
The image zooming is very common image processing and is applied to almost all image processing fields, the essence of image zooming is to add or delete some pixel point information on an original image through algorithm calculation, and an interpolation method is the most common algorithm. Currently, the commonly used algorithms include a nearest neighbor interpolation algorithm, a bilinear interpolation algorithm and a bicubic interpolation algorithm.
The principle of the nearest interpolation algorithm is to select nearest data to copy directly during interpolation, for example, a 3 × 3 image is enlarged to 4 × 4, newly added pixel points are nearest to 3 rd column and 3 rd row, then data of 3 rd column and 3 rd row are selected to copy directly, and other proportion images insert numerical values in the same way according to proportion. The algorithm has the advantages of simplicity, extremely small operation amount and poor visual effect of the image, and because the direct copying of data can form a strong sawtooth effect, the algorithm principle is to directly delete some pixel point information when the image is reduced, so that the image detail distortion can be caused.
The bilinear interpolation algorithm is a linear interpolation extension of an interpolation function with two variables, and the core idea is to perform linear interpolation in two directions (namely rows and columns) respectively. The principle is that the pixel value of the point to be sampled is linearly interpolated in the horizontal and vertical directions of the pixel values of a plurality of points adjacent to the pixel value in the original image, namely, the corresponding weight is determined according to the distance between the point to be sampled and a plurality of adjacent points around the point to be sampled, so that the pixel value of the point to be sampled is calculated. If an image of size M X N is enlarged to an image of size M X N, each point coordinate (X, Y) on the enlarged image corresponds to a point (X, Y) on the original image according to the enlargement ratio, which is calculated as:
the result of this calculation is that there are decimal places and the amount of data calculation increases significantly.
The bicubic interpolation algorithm is the most widely applied image scaling algorithm at present, and the difference lies in that the bicubic interpolation algorithm is not a linear relationship when calculating the weight, but is close to sine function sine which is very unfavorable for calculation in actual processing, and the interpolation coordinate value is most likely to have a decimal part, so the calculation process is most floating point operation, including floating point multiplication and addition, which is extremely unfavorable for a processor, and especially for a processor without an FPU (floating point arithmetic unit), the processing cannot be performed. Furthermore, the method is simple. As can be seen from the algorithm process, the calculated amount of the bicubic interpolation algorithm is increased sharply compared with that of the bilinear interpolation algorithm.
Disclosure of Invention
The invention provides an image scaling processing method and device, aiming at the technical problem that the image processing effect and the calculated amount cannot be considered in the conventional image scaling processing method, and the problem can be solved.
In order to realize the purpose of the invention, the invention is realized by adopting the following technical scheme:
an image scaling processing method, comprising the steps of:
acquiring the scaling of the image in the horizontal direction and the vertical direction;
respectively acquiring interpolation positions of interpolation points of the target image in the horizontal direction and the vertical direction in the original image;
determining all relevant pixel points corresponding to the interpolation positions in the original image;
calculating the distance between the related pixel point and the interpolation position, and performing integer processing on the distance when the scaling is non-integer;
calculating the weight coefficient of the related pixel points according to the distance after the integer processing;
and calculating the gray value at the interpolation position according to the weight coefficient and the gray value of the related pixel point, and taking the gray value as the gray value of the interpolation point of the target image at the interpolation position.
Further, the method further comprises a step of determining an interpolation radius d2, and the determination method of the related pixel point is as follows: and taking the interpolation position as a center, and taking all pixel points within the interpolation radius as corresponding related pixel points of the interpolation position in the original image.
Further, the method also comprises the steps of determining a calculated radius d 1;
will be related to the pixelWeighting factor amplification of points n3And (4) calculating:
the gray value of the target image interpolation point is in linear relation with the weight coefficient, and the equal scale reduction n is carried out according to the gray value calculated by the weight coefficient q' (x)3The multiple is taken as the gray value of the interpolation point of the target image at the interpolation position.
Further, n is equal to the denominator in the simplest fraction of the scale B.
Further, before the integer processing of the scaling B, the simplest fraction of the scaling B is simplified
When the denominator B is a multi-digit number and a +. DELTA.t 1 and B +. DELTA.t 2 have common divisor, dividing a +. DELTA.t 1 and B +. DELTA.t 2 to obtain the simplest score as the simplest score of the scaling B, wherein. DELTA.t 1 is a numerator error and. DELTA.t 2 is a denominator error.
Furthermore, the absolute delta t1 is more than or equal to 0 and less than a and 10 percent,
0≤|△t2|＜b*10％。
further, the image is scaled by B
Wherein M is the size of the original image in the horizontal direction, and M is the size of the target image in the horizontal direction, or M is the size of the original image in the vertical direction, and M is the size of the target image in the vertical direction.
Further, the gray value at the interpolation position: s (j + t, k + u) ═ Σ sq, where (j + t, k + u) is a coordinate value of the interpolation position, s is a related pixel point corresponding to the interpolation position, and q is a weight coefficient of the related pixel point.
Further, the interpolation radius d2 is 2 pixels, the calculation radius d1 is 1 pixel, and the coordinate value of the interpolation position is an integral multiple of the scaling ratio of the target image in the interpolation direction.
The invention also proposes an image scaling processing device which performs processing according to any of the image scaling processing methods described above.
Compared with the prior art, the invention has the advantages and positive effects that: the image zooming processing method of the invention carries out integer processing on the distance; the weight coefficient of the related pixel point is calculated according to the distance after the integer processing, only integer operation exists, the calculated amount is effectively reduced, the requirement on the processor is low, the whole calculation process is easy to realize for a low-configuration processor, and the calculation pressure on a high-configuration processor is smaller; and the numerical value is amplified and reduced in equal proportion, no error is introduced, and the calculation precision is higher.
Other features and advantages of the present invention will become more apparent from the following detailed description of the invention when taken in conjunction with the accompanying drawings.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flowchart of an embodiment of an image scaling method according to the present invention;
FIG. 2 is an image of a prior art sine function;
fig. 3 is an image of a function used in an embodiment of the image scaling method according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings and examples.
Example one
The image zooming is to add or delete some pixel point information on the original image, the addition of the pixel point information is called image magnification, and the reduction of the pixel point information is called image reduction. The embodiment provides an image scaling processing method, which comprises the following steps:
acquiring the scaling of the image in the horizontal direction and the vertical direction; because the image is a two-dimensional image and consists of a plurality of rows (horizontal direction) and a plurality of columns (vertical direction) of pixel points, when the image is zoomed, the rows and the columns respectively have a zoom ratio, and interpolation calculation is respectively carried out in the horizontal direction and the vertical direction in the scheme.
Interpolation in the horizontal direction and the vertical direction respectively: respectively acquiring interpolation positions of interpolation points of the target image in the horizontal direction and the vertical direction in the original image;
for example, a 2 x 2 image is enlarged to 7 x 7 and scaled to
Assuming that the size of the enlarged image (target image) is consistent with that of the original image, the abscissa values of the interpolation positions of the interpolation points in the horizontal direction of the target image in the original image are respectively
That is, the fraction may not be divided into fractions which are positive integer multiples of the scaling ratio, and a large number of floating points may be generated, resulting in a very large amount of calculation. The coordinate value calculation method of the corresponding interpolation position of each interpolation point in the vertical direction of the target image in the original image is the same as the calculation method in the horizontal direction.
For convenience of calculation, it is preferable that the coordinate value of the interpolation position is an integral multiple of the scaling ratio of the target image in the interpolation direction.
Originally, because the coordinates of each pixel point in the original image are integers, the distance between each pixel point and the interpolation position in the original image is correspondingly decimal. And the fractional part is related to scaling.
The image scaling method of this embodiment still adopts interpolation method to calculate, the gray value of the interpolation point is related to the gray value of its surrounding pixels, and the correlation degree is related to the distance between the surrounding pixels and the interpolation point, the closer the distance is, the greater the correlation is, therefore, before calculating the gray value of the interpolation point, all the related pixel points corresponding to the interpolation position in the original image are determined;
one of the key parameters for scaling the image by using the interpolation method is a weight coefficient of a related pixel point, and the weight coefficient is related to a distance;
calculating the weight coefficient of the related pixel points according to the distance after the integer processing, wherein only integer operation exists at the moment, and the calculated amount is effectively reduced;
and calculating the gray value at the interpolation position according to the weight coefficient and the gray value of the related pixel point, and taking the gray value as the gray value of the interpolation point of the target image at the interpolation position.
The manner in which the distance is subjected to the integer processing is related to the relationship between the weight coefficient and the distance.
For example, if a linear interpolation algorithm is used, the weight coefficient and the distance have a linear relationship, and when the distance is subjected to an integer processing, the coefficient of the distance is multiplied by n times, where n is a positive integer multiple of the denominator in the simplest fraction of the scaling.
For example, the weight
Then n is equal to 1 times the denominator (which may be an integer multiple greater than 1), i.e., n is equal to 7, and the weight calculated after the integer is taken
The gray value of the interpolation point is calculated using the weight value of 5. Floating point numbers do not appear any more, and the calculation amount is simplified.
Because the gray value and the weight coefficient are in a linear relation, the gray value of the interpolation point calculated by the weight value of 5 is divided by 5, and the gray value is the actual gray value of the interpolation point. Because the whole calculation process only amplifies the numerical value and then reduces the numerical value in an equal proportion, no error exists, and the method has the advantage of high accuracy.
The related pixel point is related to the interpolation radius, the present embodiment further includes a step of determining the interpolation radius, and the method for determining the related pixel point is as follows: and taking the interpolation position as a center, and taking all pixel points within the interpolation radius as related pixel points corresponding to the interpolation position in the original image. And considering that the correlation between the gray value of the pixel point within the interpolation radius and the value of the interpolation position is maximum, so that the correlation pixel point participates in the gray value calculation of the interpolation position.
The image obtained by the linear interpolation method loses part of detail information, the fineness is still low, the effect of the bicubic interpolation algorithm is very good, but the calculation amount is extremely large, and the requirement on a processor is high.
The expression of the Singer function is as follows:
in the field of digital signal processing, the more common expressions are:
the image of the sine function is shown in FIG. 2.
This embodiment is implemented by using an alternative algorithm whose function is very close to the singer function, but is relatively easy to calculate, and its expression is as follows:
wherein x is the distance between the relevant pixel point and the interpolation position, q (x) is the weight coefficient of the relevant pixel point with the distance x from the interpolation position, d1 is the calculation radius, d2 is the interpolation radius, and therefore, before calculating the weight coefficient, the method further comprises the step of determining the calculation radius d 1. The image of this function can be seen in fig. 3.
The calculation radius d1 and the interpolation radius d2 may be selected according to actual needs, for example, in this embodiment, the optional interpolation radius d2 is 2 pixels, and the calculation radius d1 is 1 pixel.
Even if the weight coefficient is calculated by adopting the function, when the distance has a decimal number, floating point number multiplication needs to be carried out, the floating point number multiplication (one cubic calculation and one quadratic calculation) needs to be carried out at least 5 times for calculating the weight in the horizontal direction for each point, for 4 x 4 images, 20 floating point number multiplications are carried out for 4 x 4 points in a row, and 20 floating point number multiplications are carried out in the vertical direction, and finally, the weighted sum of gray values is 16 floating point number multiplications. In general, the computation time of one floating-point number multiplication is tens of times or even tens of times of that of integer operation. Moreover, the actual image is not so small, and the amount of calculation is further exponentially multiplied.
In this embodiment, when the weight coefficient is calculated by using the piecewise function, the distance is processed by an integer, specifically: the distance integer is obtained by amplifying the weight coefficient of the related pixel point by n3And (4) calculating:
the gray value of the target image interpolation point is in linear relation with the weight coefficient, and the equal scale reduction n is carried out according to the gray value calculated by the weight coefficient q' (x)3The multiple is taken as the gray value of the interpolation point of the target image at the interpolation position.
The gray value is reduced by equal proportion n3Multiplying the calculated gray value by 1/n3And multiplying to obtain the actual gray value.
Still taking the example of enlarging a 2 x 2 image to 7 x 7, the scaling is
n is 7, the distance x between the relevant pixel point and the interpolation position is the scaling ratio of
Can be expressed as an integral multiple of
N is an integer, and N is amplified by the weight coefficient of the related pixel point3After the calculation of the times:
from the above, the whole calculation is all integer calculation, floating point calculation is not needed any more, and the calculation amount is greatly reduced.
The scaling B of the image in this embodiment is
Wherein M is the size of the original image in the horizontal direction, and M is the size of the target image in the horizontal direction, or M is the size of the original image in the vertical direction, and M is the size of the target image in the vertical direction.
The horizontal direction interpolation and the numerical direction interpolation need to be calculated according to the scaling of the direction respectively, and the calculation modes are the same.
N is preferably selected to be the denominator in the simplest fraction of the scaling ratio B, namely 1 time of the denominator, so that fraction calculation can be eliminated, the integer numerical value can be reduced, and the calculation amount is further reduced.
For example, if a 2 × 2 image is enlarged to 7 × 7, the ratio is 2: when the integral number processing is performed, the weight coefficient may be increased by 7 × 7, and then the calculated gradation value may be decreased by 7 × 7. However, if the scaling is 113/222, the denominator is particularly large, which results in large integer calculation, and the integer processing also results in large calculation amount, and to solve this problem, the embodiment further includes simplifying the simplest fraction of the scaling B before the scaling B is subjected to the integer processing
When the denominator b is a multi-digit number(two or more digits), and a +. DELTA.t 1 and B +. DELTA.t 2 have common divisor, a +. DELTA.t 1 and B +. DELTA.t 2 are divided into simple scores, the obtained simple scores are used as the simplest scores of the scaling B, Deltat 1 is a numerator error, Deltat 2 is a denominator error, and Deltat 1 and Deltat 2 are integers.
For example, the original scale is 113/222, the image scale can be changed to 113/226 or 110/220, the reason for this can be that there is almost no difference in the sizes of several pixels in human vision or recognition system, the numerator or denominator and the error are reduced after additive calculation, the obtained simplest score is 1/2, and the integer calculation amount can be effectively reduced. Δ t1 and Δ t2 may be positive integers, negative integers, or 0.
In order to prevent the simplified operation from introducing large errors, it is preferable that 0 ≦ Δ t1| < a × 10%,
0≤|△t2|＜b*10％。
gray value at interpolation position: s (j + t, k + u) ═ Σ sq, where s (j + t, k + u) is the gray level value of the interpolation position with the coordinate value of (j + t, k + u), s is the gray level value of the relevant pixel point corresponding to the interpolation position, and q is the weight coefficient of the relevant pixel point s.
Example two
This embodiment proposes an image scaling processing apparatus that performs processing according to the image scaling processing method described in the first embodiment. For details, reference may be made to the description in the first embodiment, which is not described herein again.
The processing method of the image zooming processing device solves the problem of integer number from the root, does not introduce errors, is all integer operation, has low requirement on a processor for the calculated amount, is easy to realize for a low-configuration processor in the whole process, has smaller calculation pressure for a high-configuration processor and has higher calculation precision.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (10)
1. An image scaling processing method, characterized by comprising the steps of:
acquiring the scaling of the image in the horizontal direction and the vertical direction;
respectively acquiring interpolation positions of interpolation points of the target image in the horizontal direction and the vertical direction in the original image;
determining all relevant pixel points corresponding to the interpolation positions in the original image;
calculating the distance between the related pixel point and the interpolation position, and performing integer processing on the distance when the scaling is non-integer;
calculating the weight coefficient of the related pixel points according to the distance after the integer processing;
and calculating the gray value at the interpolation position according to the weight coefficient and the gray value of the related pixel point, and taking the gray value as the gray value of the interpolation point of the target image at the interpolation position.
2. The image scaling method according to claim 1, further comprising a step of determining an interpolation radius d2, wherein the determination method of the related pixel point is: and taking the interpolation position as a center, and taking all pixel points within the interpolation radius as corresponding related pixel points of the interpolation position in the original image.
3. The image scaling processing method according to claim 2,
further comprising determining a calculated radius d 1;
amplifying the weight coefficient of the related pixel point by n3And (4) calculating:
target image interpolationThe gray value of the value point is in linear relation with the weight coefficient, and the equal scale reduction n is carried out according to the gray value calculated by the weight coefficient q' (x)3The multiple is taken as the gray value of the interpolation point of the target image at the interpolation position.
4. The image scaling processing method of claim 3, wherein n is equal to a denominator in a simplest score of the scaling ratio B.
5. The image scaling method of claim 3, wherein the step of reducing the simplest score of the scaling B is further included before the step of performing the integer processing on the scaling B
When the denominator B is a multi-digit number and a + Δ t1 and B + Δ t2 have common divisor, a + Δ t1 and B + Δ t2 are divided, the obtained simplest score is used as the simplest score of the scaling ratio B, Δ t1 is a numerator error, and Δ t2 is a denominator error.
6. The image scaling processing method according to claim 5,
0≤|Δt1|＜a*10％，
0≤|Δt2|＜b*10％。
7. the image scaling method according to any of claims 1 to 6, wherein the scaling B of the image is
Wherein M is the size of the original image in the horizontal direction, and M is the size of the target image in the horizontal direction, or M is the size of the original image in the vertical direction, and M is the size of the target image in the vertical direction.
8. The image scaling processing method according to any one of claims 1 to 6, wherein the gray value at the interpolation position: s (j + t, k + u) ═ Σ sq, where s (j + t, k + u) is the gray level value of the interpolation position whose coordinate value is (j + t, k + u), s is the gray level value of the relevant pixel point corresponding to the interpolation position, and q is the weight coefficient of the relevant pixel point.
9. The image scaling processing method according to any one of claims 1 to 6, wherein the interpolation radius d2 is 2 pixels, the calculation radius d1 is 1 pixel, and the coordinate value of the interpolation position is an integer multiple of the scaling ratio of the target image in the interpolation direction.
10. An image scaling processing apparatus characterized by performing processing according to the image scaling processing method of any one of claims 1 to 9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010747284.6A CN111968033A (en) | 2020-07-29 | 2020-07-29 | Image scaling processing method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010747284.6A CN111968033A (en) | 2020-07-29 | 2020-07-29 | Image scaling processing method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111968033A true CN111968033A (en) | 2020-11-20 |
Family
ID=73363638
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010747284.6A Pending CN111968033A (en) | 2020-07-29 | 2020-07-29 | Image scaling processing method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111968033A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112995533A (en) * | 2021-02-04 | 2021-06-18 | 上海哔哩哔哩科技有限公司 | Video production method and device |
| CN113592714A (en) * | 2021-08-05 | 2021-11-02 | 杭州雄迈集成电路技术股份有限公司 | Image amplification method, module and system |
| CN113593707A (en) * | 2021-09-29 | 2021-11-02 | 武汉楚精灵医疗科技有限公司 | Stomach early cancer model training method and device, computer equipment and storage medium |
| CN113763250A (en) * | 2021-09-10 | 2021-12-07 | 四川创安微电子有限公司 | Adaptive image scaling processing method and system |
| CN114679547A (en) * | 2022-04-07 | 2022-06-28 | 海宁奕斯伟集成电路设计有限公司 | Image processing apparatus, method, and program |
-
2020
- 2020-07-29 CN CN202010747284.6A patent/CN111968033A/en active Pending
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112995533A (en) * | 2021-02-04 | 2021-06-18 | 上海哔哩哔哩科技有限公司 | Video production method and device |
| CN113592714A (en) * | 2021-08-05 | 2021-11-02 | 杭州雄迈集成电路技术股份有限公司 | Image amplification method, module and system |
| CN113763250A (en) * | 2021-09-10 | 2021-12-07 | 四川创安微电子有限公司 | Adaptive image scaling processing method and system |
| CN113763250B (en) * | 2021-09-10 | 2023-09-01 | 四川创安微电子有限公司 | Adaptive image scaling processing method and system |
| CN113593707A (en) * | 2021-09-29 | 2021-11-02 | 武汉楚精灵医疗科技有限公司 | Stomach early cancer model training method and device, computer equipment and storage medium |
| CN113593707B (en) * | 2021-09-29 | 2021-12-14 | 武汉楚精灵医疗科技有限公司 | Stomach early cancer model training method and device, computer equipment and storage medium |
| CN114679547A (en) * | 2022-04-07 | 2022-06-28 | 海宁奕斯伟集成电路设计有限公司 | Image processing apparatus, method, and program |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111968033A (en) | Image scaling processing method and device | |
| US7043091B2 (en) | Method and apparatus for increasing spatial resolution of an image | |
| US10614551B2 (en) | Image interpolation methods and related image interpolation devices thereof | |
| EP2383701B1 (en) | Image processing method and apparatus | |
| CN110223232A (en) | A kind of video image amplifying method based on bilinear interpolation algorithm | |
| Chen | VLSI implementation of an adaptive edge-enhanced image scalar for real-time multimedia applications | |
| JP3865165B2 (en) | How to enlarge / reduce image data | |
| CN106780336B (en) | Image reduction method and device | |
| US5995682A (en) | Method for resizing of a digital image | |
| JP2007065039A (en) | Image processing apparatus | |
| CN114024944B (en) | Media content implantation method, device, electronic equipment and storage medium | |
| CN109325909B (en) | Image amplification method and image amplification device | |
| US8472756B2 (en) | Method for producing high resolution image | |
| JPH1049665A (en) | Device and method for image processing | |
| Liu et al. | Image enlargement method based on cubic surfaces with local features as constraints | |
| US20230082346A1 (en) | Image processing methods, electronic devices, and non-transitory computer-readable storage media | |
| TWI406187B (en) | Fast and high quality image/video interpolation method and apparatus | |
| US20220020113A1 (en) | Image resizing using seam carving | |
| Siva et al. | A low cost high performance VLSI architecture for image scaling in multimedia applications | |
| CN111784733B (en) | Image processing method, device, terminal and computer readable storage medium | |
| CN115809959A (en) | Image processing method and device | |
| CN111093045B (en) | Method and device for scaling video sequence resolution | |
| JP2000187726A (en) | Data interpolation method and its device and storage medium | |
| US6388673B1 (en) | Method and system for filter type approximation in computer graphics processing | |
| Liu et al. | 32‐2: Improvement in Directional Cubic Convolution Image Interpolation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20201120 |
|
| RJ01 | Rejection of invention patent application after publication |