CN100426831C - Image sharpening method and apparatus - Google Patents
Image sharpening method and apparatus Download PDFInfo
- Publication number
- CN100426831C CN100426831C CNB2006100057359A CN200610005735A CN100426831C CN 100426831 C CN100426831 C CN 100426831C CN B2006100057359 A CNB2006100057359 A CN B2006100057359A CN 200610005735 A CN200610005735 A CN 200610005735A CN 100426831 C CN100426831 C CN 100426831C
- Authority
- CN
- China
- Prior art keywords
- pixel
- sharpening
- image
- horizontal
- square
- 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.)
- Expired - Fee Related
Links
Images
Abstract
The present invention relates to an image sharpening method and a device thereof. A plurality of sharpening grey scale increments of the present pixel are determined according to a non-linear vision function and gray-scale value difference between the present pixel and each contiguous pixel. Then, all sharpening grey scale increments are added to be used as the total increment of the sharpening grey scales, and the total increment of the sharpening grey scales is added with the gray-scale value of the present pixel to be used as a sharpening gray-scale value of the present pixel. The sharpening gray-scale value of the present pixel is output to be used as a sharpening enhancement image.
Description
Technical field
The present invention relates to a kind of image sharpening method, particularly relate to a kind of image sharpening method that is applicable to the operating environment that literal and image mix.
Background technology
Traditional image sharpening method is to utilize the gray scale difference value (gray level difference) with two adjacent pixels (pixel) in the image to amplify, therefore strengthened the amplitude on striograph limit, make human eye clearly be picked out this two adjacent pixel, therefore make image definitionization, but and improved identification simultaneously.
Fig. 1 is the pixel schematic diagram of expression one dimension (1D) image, wherein the gray value of i pixel of X (i) expression.Traditional image sharpening method is a reference linear visibility function as shown in Figure 2, according to the former gray scale difference value between pixel i and pixel (i+1) | X (i+1)-X (i) |, the linear visibility function of contrast Fig. 2 is obtained a gray scale recruitment (gray level increment) Δ L.Then the gray value X (i) with the i pixel deducts this gray scale recruitment Δ L, obtains a new gray value X (i) '=(X (i)-Δ L) of i pixel.(i+1) pixel then is that former gray value X (i+1) is added this gray scale recruitment Δ L, obtains a new gray value X (i+1) '=(X (i+1)+Δ L) of (i+1) pixel, to export as the image after the sharpening.Fig. 3 is remarked pixel i and the gray value of pixel (i+1) before and after sharpening is handled.By Fig. 3 can clearly be seen that handle through sharpening after, the gray scale difference value between this two adjacent pixel is by original | X (i+1)-X (i) | become | X (i+1)-X (i) |+| 2 Δ L|, its grey scale change amount increases, thereby makes former image definitionization.
But when being to use the traditional image sharpening method of this kind in word graph shelves, can producing overshoot (overshoot) at the edge of literal, thereby cause encircling side effect, that is will produce a white edge (white ring) in the outer rim of black literal.Therefore need a kind of image sharpening method that is applicable to the operating environment that literal and image mix, under the situation of possessing the image sharpening effect, the ring side effect that the literal that can diminish edge forms.
Summary of the invention
Because above-mentioned situation, the present invention proposes a kind of image sharpening method, and it comprises according to a non-linear visual function and the current pixel gray value differences between each neighbor of current pixel therewith, a plurality of sharpening gray scale recruitments of decision current pixel.Again with each sharpening gray scale recruitment addition, with as a total sharpening gray scale recruitment, and with the gray value addition of total sharpening gray scale recruitment and current pixel, with the sharpening gray value as current pixel.
The present invention also proposes a kind of image sharpening method, it comprises according to the gray value differences between each neighbor of a non-linear visual function and a current pixel and current pixel, a plurality of sharpening gray scale recruitments of decision current pixel, wherein when the gray value differences between one first pixel and one second pixel during less than a reference value, the sharpening gray scale recruitment of the gray value differences of then corresponding respectively current pixel and first pixel and second pixel is a default recruitment.Above-mentioned first pixel and second pixel are adjacent with current pixel, and current pixel is between first pixel and second pixel.Then with each sharpening gray scale recruitment addition, with as a total sharpening gray scale recruitment.According to the gray value differences between each the horizontal neighbor in the image unit that comprises current pixel, determine whether this image unit is a horizontal texture square then.Whether be that the result of horizontal texture square changes a horizontal texture variable value according to image unit again, with according to horizontal texture parameter and the total sharpening gray scale of horizontal texture threshold value adjustment recruitment, and with the gray value addition of total sharpening gray scale recruitment and current pixel, with a sharpening gray value as current pixel.
In addition, the present invention proposes a kind of image sharpening device, and it comprises a pixel grey scale comparator and a sharpening processor.The pixel grey scale comparator receives the gray value between each neighbor of the gray value of the current pixel in an image data and the comparison image data and current pixel, to produce a plurality of gray value differences.And the sharpening processor, be coupled to the pixel grey scale comparator, according to each gray value differences and a non-linear visual function, a plurality of sharpening gray scale recruitments of decision current pixel, and with each sharpening gray scale recruitment addition, with as a total sharpening gray scale recruitment, and with the gray value addition of total sharpening gray scale recruitment and current pixel, with the sharpening gray value as current pixel.
For above-mentioned purpose of the present invention, feature and advantage can be become apparent, preferred embodiment cited below particularly, and be described with reference to the accompanying drawings as follows.
Description of drawings
Fig. 1 is the pixel schematic diagram of a 1D image.
Fig. 2 is the schematic diagram of a linear visibility function.
Fig. 3 is the schematic diagram of pixel i and the gray value of pixel (i+1) before and after sharpening is handled.
Fig. 4 is the image process unit schematic diagram of a 3X3 pixel.
Fig. 5 is the method flow diagram according to the described image sharpening of the embodiment of the invention.
Fig. 6 is the schematic diagram of a non-linear visual function.
Fig. 7 is the schematic diagram of a sharpening gray scale recruitment of each neighbor of correspondence of the image process unit of Fig. 4.
Fig. 8 A and 8B represent that respectively three neighbors appear gray value schematic diagram under the formula noise situations suddenly in nothing.
Fig. 9 is the pixel schematic diagram of the required comparison when appearing the formula noise reduction suddenly.
Figure 10 is the horizontal neighbor schematic diagram of the required comparison when carrying out the horizontal texture detection.
Figure 11 one comprises an image of the image process unit of Fig. 4.
Figure 12 is the vertical adjacent pixels schematic diagram of the required comparison when carrying out the vertical texture detection.
Figure 13 is the calcspar according to the described image sharpening device of the embodiment of the invention.
Figure 14 is an embodiment calcspar of the sharpening processor of Figure 13.
Figure 15 is an embodiment schematic diagram of the horizontal texture detector of Figure 14.
The reference numeral explanation:
130～image sharpening device; 132～pixel grey scale comparator;
134～sharpening processor; 141～memory;
142～recruitment generator; 143～stride the grey scale pixel value comparator;
144～multiplexer; 145～horizontal texture detector;
146～vertical texture detector; 147,148～multiplier;
149～adder; 150～texture detector;
152～horizontal texture multiplexer; 154～adder;
156～horizontal texture counter.
Embodiment
Fig. 4 represents the image process unit 40 of a 3X3 pixel, and wherein A～I respectively represents a pixel.Fig. 5 is an image sharpening method flow chart 500 according to an embodiment of the invention.In step S502, according to as shown in Figure 6 a non-linear visual function, and the gray value differences between each neighbor of a current pixel and this current pixel, determine the sharpening gray scale recruitment of this current pixel.Then in step S504, with each sharpening gray scale recruitment addition, with as a total sharpening gray scale recruitment.Then, can proceed directly to step S514, with this total sharpening gray scale recruitment gray value addition of current pixel therewith, with a sharpening gray value as this current pixel.Suppose that the current pixel of handling now is the pixel E of image process unit 40, and its former gray value is X
EIn step S502, according to non-linear visual function shown in Figure 6 and the neighbor of pixel E (gray value differences of that is pixel A～D and F～I) | X
E-X
A|～| X
E-X
D| and | X
E-X
F|～| X
E-X
1| obtain a sharpening gray scale recruitment Δ L of each neighbor of correspondence as shown in Figure 7
A～Δ L
DAnd Δ L
F～Δ L
IWherein, non-linear visual function shown in Figure 6 is that the gray scale difference of adding up the nature image distributes obtained with literal image greyscale difference cloth.As with current pixel for and neighbor A be example, obtain the gray value differences between pixel E and pixel A | X
E-X
A| after, again according to non-linear visual function shown in Figure 6, can obtain the sharpening gray scale recruitment Δ L of respective pixel A
A, follow the sharpening gray scale recruitment Δ L that obtains respective pixel B～D and F～I according to identical method
B～Δ L
DAnd Δ L
F～Δ L
I
Yet, when carrying out the image sharpening processing, need to consider to appear suddenly the interference problem of formula noise (spike noise).Figure 8 shows that the gray value schematic diagram of three neighbor P1～P3, wherein Fig. 8 A and 8B are respectively and have or not the situation of appearing formula noise (spike noise) suddenly.Wherein, shown in Fig. 8 A, when the gray value differences of the both sides of pixel P2 neighbor P1 and P3 | X
P1-X
P3| during less than a reference value, then pixel P2 looks one and appears the formula noise suddenly.Otherwise, when the gray value differences of the both sides of the pixel P2 shown in Fig. 8 B neighbor P1 and P3 | X
P1-X
P3| during greater than a reference value, then do not appear formula interference of noise problem suddenly.When the formula of appearing suddenly the noise that occurs shown in Fig. 8 A, then should in handling, sharpening not strengthen the sharpening degree of pixel P2.Therefore, in one embodiment of this invention, when carrying out step S502, if the right and left pixel of current pixel, when the gray value differences of both sides pixel and both sides, oblique angle pixel is less than a reference value up and down, just make the corresponding the right and left pixel of this current pixel up and down the sharpening gray scale recruitment of the gray value differences of both sides pixel and both sides, oblique angle pixel be a default recruitment Δ L
FixedAs shown in Figure 9, when processed pixels E, then need consider the gray value differences between adjacent pixels G and C and pixel A and I on left and right sides neighbor D and F, neighbouring pixel B and H and the diagonal line.Left and right sides neighbor D and F with pixel E are example, when | X
D-X
F|＜Δ L
Fixed, then make the respective pixel D of pixel E and the sharpening gray scale recruitment Δ L of F
DAnd Δ L
FBe Δ L
FixedIn one embodiment, this default recruitment is a Δ
LfixedBe 0.
Then carry out step S504, with sharpening gray scale recruitment Δ L
B～Δ L
DAnd Δ L
F～Δ L
IAddition is as the total sharpening gray scale recruitment Δ L of pixel E
Total_EAt this moment, can proceed directly to step S514 in one embodiment of this invention, with this total sharpening gray scale recruitment Δ L
Total_EAdd pixel E gray scale X originally
E, that is the sharpening gray value X of pixel E
E'=X
E+ Δ L
Total_EIn other embodiment, also can carry out step S506～S512 hereinafter at the detection of image texture.
If having continuous colourity originally in the image changes, then carry out to produce after sharpening is handled discontinuous spot output herein, therefore in one embodiment, can proceed step S506, according to the gray value differences between each the horizontal neighbor in the image unit that comprises current pixel, whether the decision image unit is a horizontal texture square.With image process unit shown in Figure 4 40 is example, and when current pixel was pixel E, then image unit was a 3X3 pixel image unit that comprises pixel A～I as shown in Figure 4.In step S506, at first determine the gray value differences between each horizontal neighbor in this image unit, that is as shown in figure 10, gray value differences between decision pixel A and B, B and C, D and E, E and F, G and H and H and I determines according to the gray value differences that obtains whether this image unit is square in the horizontal line again.In one embodiment, its judgment mode is each gray value differences and a horizontal gray scale difference reference value Δ X between the horizontal neighbor that will obtain
Hori.Relatively, change the value of a horizontal square parameter h_priority again according to result relatively.After all horizontal neighbors in this image unit are relatively finished successively, if last horizontal square parameter h_priority value is greater than a horizontal square reference value n
_ hori.The time, then this image unit is a horizontal texture square.Pixel A and B with image process unit 40 are example, when | X
A-X
B|＜Δ X
Hori.The time, then parameter h_priority adds 1, follows processed pixels B and C, D and E, E and F, G and H and H and I successively.Suppose horizontal square reference value n
_ hori.Be 3, then if last parameter h_priority is 5, then image process unit 40 is a horizontal texture square.
Shown in Figure 11 is one to comprise an image of image process unit 40.Because of when carrying out the image sharpening processing, determine total sharpening gray scale recruitment Δ L
Total_EThe time, in the horizontal texture shown in step S506 of carrying out is judged, also need consider to belong in the same image horizontal texture with the pixel of delegation.Therefore determine in above-mentioned steps S506 that whether an image unit is after the horizontal texture square, carry out step S508, whether according to this image unit is that the result of horizontal texture square changes a horizontal texture variable value hcounter, adjusts total sharpening gray scale recruitment Δ L according to this a horizontal texture parameter hcounter and a horizontal texture threshold value n again
TotalSuppose that image shown in Figure 10 is the image of a 1024X768 pixel, then at the total sharpening gray scale recruitment Δ L that determines pixel E
Total_EThe time, be the determined level texture, also need consider 1023 pixels of m on capable that coexist with pixel E.As above-mentioned, when the image unit 40 of pixel E is a horizontal texture square, then can make horizontal texture variable value hcounter add 1, wherein, horizontal texture variable value hcounter is with relevant with the pixel E pixel of m on capable that coexist.With Figure 10 is example, because of horizontal texture variable value hcounter when the image unit 90 at processed pixels D place, its value is 3; And the image unit 40 that comprises pixel E is a horizontal texture square, so horizontal texture parameter hcounter adds 1, that is hcounter=4; Wherein, horizontal texture parameter hcounter＜horizontal texture threshold value n is because Figure 10 is the image of a 1024X768 pixel, so n is 1024.In addition, if in step S506, determine that this image unit is not a horizontal texture square, then horizontal texture parameter hcounter subtracts 1.Then, adjust total sharpening gray scale recruitment Δ L of pixel E according to horizontal texture parameter hcounter and horizontal texture threshold value n
Total_EIn one embodiment, adjusted Δ L
Total_E'=Δ L
Total_E* (n-hcounter)/n.
In one embodiment, also can judge the vertical texture of an image unit.In step S510, according to the gray value differences between each vertical adjacent pixels in the image unit that comprises current pixel, whether the decision image unit is a vertical texture square.Figure 12 is the vertical adjacent pixels that expression image unit 40 carries out the required comparison of vertical texture when detecting.Wherein in step S510, also comprise the vertical gray scale difference reference value of gray value differences between each vertical adjacent pixels of this image unit of comparison with one, and according to each comparative result change one vertical square variable value, if vertical square parameter is greater than a vertical square reference value, this image unit vertical texture square for this reason then.Those skilled in the art should implement this step according to execution mode shown in the above-mentioned steps S506 and notion, so do not repeat them here.
After whether this image unit of decision is a vertical texture square, proceed step S512, according to this image unit whether for this reason the result of vertical texture square change a vertical texture variable value, adjust this total sharpening gray scale recruitment according to this a vertical texture parameter and a vertical texture threshold value again.Step S508 as mentioned is described for its detailed execution mode, also repeats no more at this.
It should be noted that the user looks its needs, can promptly directly carry out step S514 after finishing step S502 and 504 as shown in above, the image (as the arrow Y1 of Fig. 5) after handling with the output sharpening.Also can consider after step S504, to detect one of image level or vertical texture, respectively shown in the arrow Y2 and Y3 of Fig. 5; Or be after completing steps S504,, carry out the detection of image level and vertical texture in regular turn, carry out step S514 again, the image after handling with the output sharpening according to step S506～S512.Those skilled in the art can be according to required, and the principle suggested according to the present invention is to adjust the embodiment flow process.
Figure 13 represents image sharpening device 130 according to another embodiment of the present invention.Image sharpening device 130 receives a former image data, and this image data is made image sharpening handle, and exports a sharpening again and strengthens image.Image sharpening device 130 comprises a pixel grey scale comparator 132, and a sharpening processor 134.Pixel grey scale comparator 132 receives these former image datas, and the gray value gray value between each neighbor of current pixel therewith of the current pixel in this former image data relatively, to produce a plurality of gray value differences.Sharpening processor 134 then is coupled to pixel grey scale comparator 132, according to each gray value differences and a non-linear visual function of pixel grey scale comparator 132 outputs, determines a plurality of sharpening gray scale recruitments of this current pixel.After obtaining all sharpening gray scale recruitments, with all sharpening gray scale recruitment additions, with as a total sharpening gray scale recruitment.
Figure 14 shows that the calcspar of an embodiment of sharpening processor 134.Wherein sharpening processor 134 comprises that a memory 141, a recruitment generator 142, stride grey scale pixel value comparator 143, a multiplexer 144, a horizontal texture detector 145, a vertical texture detector 146, paired multiplier 147 and 148, and an adder 149.Memory 141 is used for depositing a non-linear visual function, when sharpening processor 134 when pixel grey scale comparator 132 receives a plurality of gray value differences, recruitment generator 142 is according to these a plurality of gray value differences that receive, and obtains corresponding sharpening gray scale recruitment in the stored non-linear visual function of memory 141.Wherein, as mentioned above, in one embodiment, appear the formula interference of noise suddenly for subduing, stride grey scale pixel value comparator 143 and can receive former image data, and be the center, obtain the gray value differences between adjacent, the vertical adjacent and oblique angle adjacent pixels of its level with a current pixel.And each is striden pixel grey scale value difference and reference value comparison, when striding the pixel grey scale value difference, then export 0, otherwise then export 1 less than this reference value.Multiplexer 144 the sharpening gray scale recruitment that receives recruitment generator 142 output and stride 143 outputs of grey scale pixel value comparator stride the comparative result of pixel grey scale value difference and reference value the time, if when striding the output 0 of grey scale pixel value comparator 143, then the sharpening gray scale recruitment with corresponding adjacent pixel is made as a default recruitment, otherwise, when striding the output 1 of grey scale pixel value comparator 143, then keep original sharpening gray scale recruitment.In one embodiment, this default recruitment is zero.At last with all sharpening gray scale recruitment additions, to export a total sharpening gray scale recruitment.
In one embodiment, can add the mechanism that horizontal texture detects.Wherein, pixel grey scale comparator 132 relatively comprises the gray value between each horizontal neighbor in the image unit of this current pixel, whether horizontal texture detector 145 is a horizontal texture square according to the gray value differences between each the horizontal neighbor in the image unit that is received from pixel grey scale comparator 132 to determine this image unit.Wherein, a gray value differences and a horizontal gray scale difference reference value between each straight horizontal neighbor of horizontal texture detector 146 comparison image units, and change a horizontal square variable value according to each comparative result, if this horizontal square parameter is greater than a horizontal square reference value, then this image unit is this horizontal texture square.Whether horizontal texture detector 145 is that the result of horizontal texture square changes a horizontal texture variable value according to this image unit then.Export multiplier 147 to adjust total sharpening gray scale recruitment of multiplexer 144 outputs according to a horizontal texture parameter and a horizontal texture threshold value then.
Similarly, also can add the vertical texture decision mechanism in one embodiment.Pixel grey scale comparator 132 relatively comprises the gray value between each vertical adjacent pixels in the image unit of this current pixel, vertical texture detector 146 according to according to the gray value differences between each vertical adjacent pixels in the image unit that is received from pixel grey scale comparator 132, determines whether this image unit is a vertical texture square again.Wherein, the vertical gray scale difference reference value of gray value differences between each vertical adjacent pixels of vertical texture detector 146 comparison image units with one, and change a vertical square variable value according to each comparative result, if this vertical square parameter is greater than a vertical square reference value, then this image unit is this vertical texture square.Whether vertical texture detector 146 is that the result of vertical texture square changes a vertical texture variable value according to this image unit then, exports multiplier 148 to adjust total sharpening gray scale recruitment according to a vertical texture parameter and a vertical texture threshold value then.
Figure 15 represents the embodiment schematic diagram of a horizontal texture detector 145.Horizontal texture detector 145 comprises a texture detector 150, a horizontal texture multiplexer 152, an adder 154 and horizontal texture counter 156.Pixel grey scale comparator 132 produces the gray value differences of each the horizontal neighbor in the image unit, 150 gray value differences that receive between these a plurality of horizontal neighbors of texture detector, again itself and a horizontal gray scale difference reference value are compared, and change a horizontal square variable value according to each comparative result, if this horizontal square parameter is greater than a horizontal square reference value, then this image unit is a horizontal texture square, and horizontal texture testing result signal H_texture inputs to horizontal texture multiplexer 152.After horizontal texture multiplexer 152 receives the testing result signal H_texture of texture detector 150, if signal H_texture shows that this image unit is a horizontal texture square, then horizontal texture multiplexer 152 outputs 1, on the contrary then export-1 to adder 154.Adder 154 is then added up the value of a last horizontal texture counter 156 and the output of horizontal texture multiplexer 152, exports horizontal texture counter 156 to.156 horizontal texture Counter Values of horizontal texture counter go out to multiplier 147, in order to adjust total sharpening gray scale recruitment of multiplexer 144 outputs.Vertical texture detector 146 also can be with reference to the horizontal texture detector 145 of Figure 15, and its implementation method should be that the personage of this skill is familiar with, thereby does not repeat them here.At last, will with a sharpening gray value, be output as sharpening and strengthen image through total sharpening gray scale recruitment gray value addition of current pixel therewith in adder 149 of horizontal texture detector 145 and 146 adjustment of vertical texture detector as current pixel.
Though the present invention discloses as above with preferred embodiment; right its is not in order to limit the present invention; those skilled in the art can do some changes and retouching under the premise without departing from the spirit and scope of the present invention, so protection scope of the present invention is as the criterion with claim of the present invention.
Claims (30)
1. image sharpening method comprises:
According to the gray value differences between each neighbor of a non-linear visual function and a current pixel and this current pixel, determine a plurality of sharpening gray scale recruitments of this current pixel;
With this each sharpening gray scale recruitment addition, with as a total sharpening gray scale recruitment; And
With the gray value addition of this total sharpening gray scale recruitment and this current pixel, with a sharpening gray value as this current pixel.
2. image sharpening method as claimed in claim 1, wherein determine the step of this sharpening gray scale recruitment of this current pixel also to comprise: when the gray value differences between first pixel and second pixel during less than a reference value, then respectively to should current pixel and this sharpening gray scale recruitment of the gray value differences of this first pixel and this second pixel be a default recruitment, wherein this first pixel and this second pixel are adjacent with this current pixel respectively, and this current pixel is between this first pixel and this second pixel.
3. image sharpening method as claimed in claim 2 should default recruitment be zero wherein.
4. image sharpening method as claimed in claim 2 also comprises:
According to the gray value differences between each the horizontal neighbor in the image unit that comprises this current pixel, determine whether this image unit is a horizontal texture square.
5. image sharpening method as claimed in claim 4 determines that wherein whether this image unit is that the step of this horizontal texture square also comprises:
Relatively a gray value differences between the horizontal neighbor of each of this image unit and a horizontal gray scale difference reference value, and change a horizontal square variable value according to each comparative result, if this horizontal square parameter is greater than a horizontal square reference value, then this image unit is this horizontal texture square.
6. image sharpening method as claimed in claim 2 also comprises:
According to the gray value differences between each vertical adjacent pixels in the image unit that comprises this current pixel, determine whether this image unit is a vertical texture square.
7. image sharpening method as claimed in claim 6 determines that wherein whether this image unit is that the step of this vertical texture square also comprises:
The vertical gray scale difference reference value of the gray value differences between each vertical adjacent pixels of this image unit relatively with one, and according to each comparative result change one vertical square variable value, if this vertical square parameter is greater than a vertical square reference value, then this image unit is this vertical texture square.
8. image sharpening method as claimed in claim 1 also comprises:
According to the gray value differences between each the horizontal neighbor in the image unit that comprises this current pixel, determine whether this image unit is a horizontal texture square.
9. image sharpening method as claimed in claim 8 determines that wherein whether this image unit is that the step of this horizontal texture square also comprises:
Relatively a gray value differences between the horizontal neighbor of each of this image unit and a horizontal gray scale difference reference value, and change a horizontal square variable value according to each comparative result, if this horizontal square parameter is greater than a horizontal square reference value, then this image unit is this horizontal texture square.
10. image sharpening method as claimed in claim 8 also comprises:
Whether according to this image unit is the result of this horizontal texture square, change with coexist the relevant horizontal texture variable value of pixel in the delegation of current pixel, again according to this horizontal texture parameter and this total sharpening gray scale recruitment of a horizontal texture threshold value adjustment.
11. image sharpening method as claimed in claim 8, wherein this image unit is one 3 * 3 pixel image units.
12. image sharpening method as claimed in claim 1 also comprises:
According to the gray value differences between each vertical adjacent pixels in the image unit that comprises this current pixel, determine whether this image unit is a vertical texture square.
13. image sharpening method as claimed in claim 12 determines that wherein whether this image unit is that the step of this vertical texture square also comprises:
The vertical gray scale difference reference value of the gray value differences between each vertical adjacent pixels of this image unit relatively with one, and according to each comparative result change one vertical square variable value, if this vertical square parameter is greater than a vertical square reference value, then this image unit is this vertical texture square.
14. image sharpening method as claimed in claim 12 also comprises:
Whether according to this image unit is the result of this vertical texture square, change with the current pixel relevant vertical texture variable value of a pixel that lists that coexists, again according to this vertical texture parameter and this total sharpening gray scale recruitment of a vertical texture threshold value adjustment.
15. an image sharpening method comprises:
According to the gray value differences between each neighbor of a non-linear visual function and a current pixel and this current pixel, determine a plurality of sharpening gray scale recruitments of this current pixel, wherein when the gray value differences between one first pixel and one second pixel during less than a reference value, then respectively to should current pixel and this sharpening gray scale recruitment of the gray value differences of this first pixel and this second pixel be a default recruitment, wherein this first pixel and this second pixel are adjacent with this current pixel respectively, and this current pixel is between this first pixel and this second pixel;
With this each sharpening gray scale recruitment addition, with as a total sharpening gray scale recruitment;
According to the gray value differences between each the horizontal neighbor in the image unit that comprises this current pixel, determine whether this image unit is a horizontal texture square;
Whether according to this image unit is the result of this horizontal texture square, change with coexist the relevant horizontal texture variable value of pixel in the delegation of current pixel, again according to this horizontal texture parameter and this total sharpening gray scale recruitment of a horizontal texture threshold value adjustment; And
With the gray value addition of this total sharpening gray scale recruitment and this current pixel, with a sharpening gray value as this current pixel.
16. image sharpening method as claimed in claim 15 should default recruitment be zero wherein.
17. image sharpening method as claimed in claim 15 determines that wherein whether this image unit is that the step of this horizontal texture square also comprises:
Relatively a gray value differences between the horizontal neighbor of each of this image unit and a horizontal gray scale difference reference value, and change a horizontal square variable value according to each result relatively, if this horizontal square parameter is greater than a horizontal square reference value, then this image unit is this horizontal texture square.
18. image sharpening method as claimed in claim 15, wherein this image unit is one 3 * 3 pixel image units.
19. image sharpening method as claimed in claim 15 also comprises:
According to the gray value differences between each vertical adjacent pixels in the image unit that comprises this current pixel, determine whether this image unit is a vertical texture square.
20. image sharpening method as claimed in claim 19 determines that wherein whether this image unit is that the step of this vertical texture square also comprises:
The vertical gray scale difference reference value of the gray value differences between each vertical adjacent pixels of this image unit relatively with one, and change a vertical square variable value according to each result relatively, if this vertical square parameter is greater than a vertical square reference value, then this image unit is this vertical texture square.
21. image sharpening method as claimed in claim 19 also comprises:
Whether according to this image unit is the result of this vertical texture square, change with the current pixel relevant vertical texture variable value of a pixel that lists that coexists, again according to this vertical texture parameter and this total sharpening gray scale recruitment of a vertical texture threshold value adjustment.
22. an image sharpening device comprises:
One pixel grey scale comparator receives an image data and the gray value between each neighbor of the gray value of the current pixel in this image data and this current pixel relatively, to produce a plurality of gray value differences; And
One sharpening processor, be coupled to this pixel grey scale comparator, according to this each gray value differences and a non-linear visual function, determine a plurality of sharpening gray scale recruitments of this current pixel, and with this each sharpening gray scale recruitment addition, with as a total sharpening gray scale recruitment, and will this total sharpening gray scale recruitment and the gray value addition of this current pixel, with a sharpening gray value as this current pixel.
23. image sharpening device as claimed in claim 22, wherein this sharpening processor is suitable for the gray value between comparison one first pixel and one second pixel, when the gray value differences between this first pixel and this second pixel during less than a reference value, then respectively to should current pixel and this sharpening gray scale recruitment of the gray value differences of this first pixel and this second pixel be a default recruitment, wherein this first pixel and this second pixel are adjacent with this current pixel respectively, and this current pixel is between this first pixel and this second pixel.
24. image sharpening device as claimed in claim 23 should default recruitment be zero wherein.
25. image sharpening device as claimed in claim 22, wherein this pixel grey scale comparator is suitable for relatively comprising the gray value between each horizontal neighbor in the image unit of this current pixel, whether and this sharpening processor is suitable for according to the gray value differences between each horizontal neighbor of this image unit, be a horizontal texture square to determine this image unit.
26. image sharpening device as claimed in claim 25, wherein this pixel grey scale comparator is suitable for a gray value differences and the horizontal gray scale difference reference value between this each horizontal neighbor of this image unit relatively, and this sharpening processor is suitable for changing a horizontal square variable value according to each comparative result, if this horizontal square parameter is greater than a horizontal square reference value, then this image unit is this horizontal texture square.
27. image sharpening device as claimed in claim 25, wherein whether be the result of this horizontal texture square to this sharpening processor if being suitable for according to this image unit, change with coexist the relevant horizontal texture variable value of pixel in the delegation of current pixel, again according to this horizontal texture parameter and this total sharpening gray scale recruitment of a horizontal texture threshold value adjustment.
28. image sharpening device as claimed in claim 22, wherein this pixel grey scale comparator is suitable for relatively comprising the gray value between each vertical adjacent pixels in the image unit of this current pixel, and this sharpening processor is suitable for according to the gray value differences between each vertical adjacent pixels of this image unit, determines whether this image unit is a vertical texture square.
29. image sharpening device as claimed in claim 28, wherein this pixel grey scale comparator is suitable for the vertical gray scale difference reference value with of gray value differences between this each vertical adjacent pixels of this image unit relatively, and this sharpening processor is suitable for changing a vertical square variable value according to each comparative result, if this vertical square parameter is greater than a vertical square reference value, then this image unit is this vertical texture square.
30. image sharpening device as claimed in claim 28, wherein whether be the result of this vertical texture square to this sharpening processor if being suitable for according to this image unit, change with the current pixel relevant vertical texture variable value of a pixel that lists that coexists, again according to this vertical texture parameter and this total sharpening gray scale recruitment of a vertical texture threshold value adjustment.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB2006100057359A CN100426831C (en) | 2006-01-06 | 2006-01-06 | Image sharpening method and apparatus |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB2006100057359A CN100426831C (en) | 2006-01-06 | 2006-01-06 | Image sharpening method and apparatus |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN1812475A CN1812475A (en) | 2006-08-02 |
| CN100426831C true CN100426831C (en) | 2008-10-15 |
Family
ID=36845134
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CNB2006100057359A Expired - Fee Related CN100426831C (en) | 2006-01-06 | 2006-01-06 | Image sharpening method and apparatus |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN100426831C (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101667405B (en) * | 2009-08-14 | 2011-09-21 | 西安龙腾微电子科技发展有限公司 | Image sharpening method for TFT-LCD color display |
| CN103544931B (en) * | 2013-10-12 | 2017-04-19 | Tcl集团股份有限公司 | Character generating method and device and application terminal |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6175659B1 (en) * | 1998-10-06 | 2001-01-16 | Silicon Intergrated Systems Corp. | Method and apparatus for image scaling using adaptive edge enhancement |
| JP2002077668A (en) * | 2000-09-01 | 2002-03-15 | Sharp Corp | Contour correction apparatus |
| JP2002083294A (en) * | 2000-09-07 | 2002-03-22 | Fuji Xerox Co Ltd | Image processor, image processing method and recording medium stored with image processing program |
| CN1422069A (en) * | 2001-11-28 | 2003-06-04 | 凌阳科技股份有限公司 | Edge enhancement method and device for digital image amplifier circuit |
| CN1620107A (en) * | 2003-11-21 | 2005-05-25 | 三星电子株式会社 | Apparatus and method of image sharpness enhancement |
| CN1708101A (en) * | 2004-06-08 | 2005-12-14 | 三星电子株式会社 | Video signal processing apparatus and method to enhance image sharpness and remove noise |
-
2006
- 2006-01-06 CN CNB2006100057359A patent/CN100426831C/en not_active Expired - Fee Related
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6175659B1 (en) * | 1998-10-06 | 2001-01-16 | Silicon Intergrated Systems Corp. | Method and apparatus for image scaling using adaptive edge enhancement |
| JP2002077668A (en) * | 2000-09-01 | 2002-03-15 | Sharp Corp | Contour correction apparatus |
| JP2002083294A (en) * | 2000-09-07 | 2002-03-22 | Fuji Xerox Co Ltd | Image processor, image processing method and recording medium stored with image processing program |
| CN1422069A (en) * | 2001-11-28 | 2003-06-04 | 凌阳科技股份有限公司 | Edge enhancement method and device for digital image amplifier circuit |
| CN1620107A (en) * | 2003-11-21 | 2005-05-25 | 三星电子株式会社 | Apparatus and method of image sharpness enhancement |
| CN1708101A (en) * | 2004-06-08 | 2005-12-14 | 三星电子株式会社 | Video signal processing apparatus and method to enhance image sharpness and remove noise |
Non-Patent Citations (4)
| Title |
|---|
| 一种基于彩色图像的锐化算法. 华继钊，郭振民，李志军，陈琳.扬州大学学报（自然科学版），第7卷第4期. 2004 |
| 一种基于彩色图像的锐化算法. 华继钊，郭振民，李志军，陈琳.扬州大学学报（自然科学版），第7卷第4期. 2004 * |
| 带噪声抑制的反锐化掩模图像增强算法. 谢明果，何剑光，徐中佑，张砚臣.现代电子技术，第2期. 2005 |
| 带噪声抑制的反锐化掩模图像增强算法. 谢明果，何剑光，徐中佑，张砚臣.现代电子技术，第2期. 2005 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN1812475A (en) | 2006-08-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106296578B (en) | Image processing method and device | |
| CN104517110B (en) | The binarization method and system of a kind of image in 2 D code | |
| CN103366170B (en) | Image binaryzation processing unit and its method | |
| US9165346B2 (en) | Method and apparatus for reducing image noise | |
| US9053385B2 (en) | Object detection device and object detection method | |
| JP5295431B2 (en) | Image processing device | |
| EP2169657A1 (en) | Image processing device, display, image processing method, and program | |
| CN109214996B (en) | Image processing method and device | |
| JP2004157979A (en) | Image motion detection apparatus and computer program | |
| CN107274364B (en) | image enhancement method and device | |
| CN101132477B (en) | Digital image processing method and device thereof | |
| US20110158554A1 (en) | Image processing apparatus, image processing method, and program | |
| CN100426831C (en) | Image sharpening method and apparatus | |
| CN111563517A (en) | Image processing method, image processing device, electronic equipment and storage medium | |
| JP5891022B2 (en) | Object detection device, surveillance camera, and object detection method | |
| US20070086059A1 (en) | Image sharpness device and method | |
| US8995765B2 (en) | Digital image processing apparatus and method | |
| CN111429437A (en) | Image non-reference definition quality detection method for target detection | |
| CN102521804A (en) | Image edge detection method based on window modified multi-level median filtering | |
| CN110147765A (en) | A kind of image processing method and device | |
| CN114693543A (en) | Image noise reduction method and device, image processing chip and image acquisition equipment | |
| JP4990240B2 (en) | Image processing apparatus and image processing program | |
| JP4978697B2 (en) | Image correction apparatus, image correction method, and image correction program | |
| JP7039215B2 (en) | Image processing equipment, image processing methods, and programs | |
| CN102447817B (en) | Image processing device and space image noise eliminating method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20081015 Termination date: 20210106 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |