CN109360164B - Picture ground color bleeding and denoising processing method - Google Patents
Picture ground color bleeding and denoising processing method Download PDFInfo
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- CN109360164B CN109360164B CN201811124180.9A CN201811124180A CN109360164B CN 109360164 B CN109360164 B CN 109360164B CN 201811124180 A CN201811124180 A CN 201811124180A CN 109360164 B CN109360164 B CN 109360164B
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- 238000003672 processing method Methods 0.000 title claims abstract description 5
- 238000005070 sampling Methods 0.000 claims abstract description 18
- 238000010586 diagram Methods 0.000 claims abstract description 14
- 230000011218 segmentation Effects 0.000 claims abstract description 8
- 238000000034 method Methods 0.000 claims description 8
- 238000005562 fading Methods 0.000 claims 1
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20021—Dividing image into blocks, subimages or windows
Abstract
The invention discloses a picture ground color bleeding and denoising processing method. The invention comprises the following steps: binaryzation is carried out on an original picture; providing the parameter value externally; creating a two-bit array DscImage; carrying out block statistics on the picture according to the edge length of the segmentation picture; acquiring the area information of the nth block; calculating a sampling point array DSB for the nth block diagram at a color sampling interval according to the RGB color values; counting the color threshold value of the nth block image; traversing each pixel point of the nth block diagram; obtaining the final DscImage; step (10), externally calculating the color threshold ratio, the sampling interval and the value of the side length of the segmentation graph through the parameter quality; repeating the steps by using the three parameter values to obtain DscImage1 and DscImage 2; sequentially traversing the pixels of the DscImage1 and DscImage2, judging, and using the traversed DscImage2 to generate a picture. The invention can prevent the problems of graying of the ground color, unclear characters and the like when the word is printed.
Description
Technical Field
The invention belongs to the field of computer image processing, and removes the background of an image or a shadow generated by light.
Background
At present, in the internet + age, from production to life, people need to use networks in all aspects. People gradually shift traditional offline behavior to online, as well as educational aspects. The students are not limited to on-line learning, but can also do homework on line, and teachers directly and remotely correct the homework. The shooting and uploading operation is combined online and offline, and has certain practicability. However, the taken picture may not be clearly affected by the factors such as light, and at this time, the picture needs to be optimized to whiten the ground color and remove noise.
Disclosure of Invention
The invention aims to provide a method for processing background color bleeding and denoising of an image, aiming at the defects of the prior art.
The technical scheme adopted by the invention for solving the technical problem comprises the following steps:
the method comprises the following steps of (1) binarizing an original picture, expressing an R value set in picture pixel RGB values by using SrcImage, expressing the R value of a certain pixel point (x, y) of the picture by using SrcImage [ x ] [ y ] (the x axis and the y axis respectively express the length and the width), and expressing the width and the height of the picture by using ImageWidth and Imageheight respectively;
step (2), externally providing parameter values: RGBThresholdPercent (color threshold ratio, unit float), SamplingInterval (sampling interval, unit int), ImageRect (segmentation graph side length, unit int)
Step (3), creating a two-bit array DscImage for storing R value information of each pixel point after picture processing, wherein the DscImage [ x ] [ y ] and the SrcImage [ x ] [ y ] coordinate correspond to each other one by one;
and (4) carrying out block statistics on the picture according to the segmentation image edge length value ImageRect in order to improve the accuracy of image processing. The width of the picture is divided into WidthNum, WidthNum ═ ImageWidth/ImageRect + (ImageWidth% ImageRect >01: 0); the height of the picture is divided into height num ═ ImageHeight/ImageRect + (ImageHeight% ImageRect >01: 0); pictures are divided into ImageNum, width num, height num.
Step (5), acquiring the area information of the nth block diagram:
the upper, lower, left and right values of the block are ItemTop, ItemBottom, ItemLeft, ItemRight, respectively:
ItemTop＝n/WidthNum*ImageRect；
ItemBottom ═ ItemTop + ImageRect, ItemBottom ═ ImageHeight if ItemBottom > ImageHeight;
ItemLeft＝n％WidthNum*ImageRect；
ItemRight ═ ItemLeft + ImageRect, ItemRight ═ ImageWidth if ItemRight > ImageWidth.
The total pixel point ItemPointTotalNum of the block:
ItemPointTotalNum＝(ItemRight–ItemLeft+1)*(ItemBottom–ItemTop+1)。
step (6), according to the RGB color values, calculating a sampling point array DSB for the nth block diagram at a color sampling interval SamplingInterval: DSB { { Ai, Weight i } }; a. theiRepresenting the current sample color value, WeightiRepresenting the current sample color value as AiWhen the color value in the block map is AiThe number of the pixel points;
step (7), counting the color threshold RGBThreshold of the nth block diagram,
circularly sampling point array DSB, accumulating sampling color value A from i-0iStatistic value of (3)iThe loop is skipped until the accumulated value is greater than ItemPointTotalNum RGBThresholdPercent. RGBThreshold ═ (Weight 0+ Weight 1+ … + Weight i)
Step (8), traversing each pixel point of the nth block diagram, if SrcImage [ x ] [ y ] < (RGBThreshold) and DscImage [ x ] [ y ] < 0, otherwise, DscImage [ x ] [ y ] < 255
And (9) calculating each minimap according to the steps (5) to (8) to obtain the final DscImage.
Step (10), externally calculating the value of parameters such as RGBThresholdPercent and the like through parameter quality
RGBThresholdPercent1＝0.6+0.15*quality；
ImageRect1＝700*quality；
SamplingInterval1＝RGBThresholdPercent1*4；
RGBThresholdPercent2＝0.45+0.15*quality；
ImageRect2＝35/quality；
SamplingInterval2＝RGBThresholdPercent2*4；
Step (11), performing steps (2) to (9) by using RGBThresholdPercent1, SamplingInterval1 and ImageRect1 to obtain DscImage 1;
performing steps (2) - (9) with RGBThresholdPercent2, SamplingInterval2, ImageRect2 to obtain DsciImage 2;
and (12) sequentially traversing the pixels of the DscImage1 and DscImage2, wherein if the DscImage1[ x ] [ y ] ═ 255, the DscImage2[ x ] [ y ] ═ 255.
And (13) using the traversed DscImage2 to generate a picture.
The value of n according to the invention is from 0;
the quality of the invention can adjust the image processing effect, the quality is more than 0.0 and less than 1.0, the standard value is 0.8, more noise can be caused when the standard value is less than 0.8, and partial text information can be lost when the standard value is more than 0.8.
The invention has the following beneficial effects:
the method is different from other image processing software, aims at processing the character and the picture, binarizes the image, changes the background color of the image into white, highlights characters, removes interference points or lines, enhances the display effect of the title characters, and shields the influence of factors such as paper, light and the like on the picture. Therefore, when students assemble subjects into words for printing, the problems of graying of the background color, unclear characters and the like can be avoided. The method is currently applied to post-session web wrong answer books.
According to the invention, through steps (10) - (13), the image processing effect is adjusted through quality, the standard value is 0.8, noise is excessive when the standard value is less than 0.8, and partial text information is lost when the standard value is more than 0.8. The positioning is accurate through quality, and noise points and text information loss are reduced.
Detailed Description
The present invention will be further described with reference to the following examples.
A picture ground color bleeding and denoising processing method specifically comprises the following steps:
the method comprises the following steps of (1) binarizing an original picture, expressing an R value set in picture pixel RGB values by using SrcImage, expressing the R value of a certain pixel point (x, y) of the picture by using SrcImage [ x ] [ y ] (the x axis and the y axis respectively express the length and the width), and expressing the width and the height of the picture by using ImageWidth and Imageheight respectively;
step (2), externally providing parameter values: RGBThresholdPercent (color threshold ratio, unit float), SamplingInterval (sampling interval, unit int), ImageRect (segmentation graph side length, unit int)
Step (3), creating a two-bit array DscImage for storing R value information of each pixel point after picture processing, wherein the DscImage [ x ] [ y ] and the SrcImage [ x ] [ y ] coordinate correspond to each other one by one;
and (4) carrying out block statistics on the picture according to the segmentation image edge length value ImageRect in order to improve the accuracy of image processing. The width of the picture is divided into WidthNum, WidthNum ═ ImageWidth/ImageRect + (ImageWidth% ImageRect >01: 0); the height of the picture is divided into height num ═ ImageHeight/ImageRect + (ImageHeight% ImageRect >01: 0); pictures are divided into ImageNum, width num, height num.
Step (5), acquiring the area information of the nth block diagram:
the upper, lower, left and right values of the block are ItemTop, ItemBottom, ItemLeft, ItemRight, respectively:
ItemTop＝n/WidthNum*ImageRect；
ItemBottom ═ ItemTop + ImageRect, ItemBottom ═ ImageHeight if ItemBottom > ImageHeight;
ItemLeft＝n％WidthNum*ImageRect；
ItemRight ═ ItemLeft + ImageRect, ItemRight ═ ImageWidth if ItemRight > ImageWidth.
The total pixel point ItemPointTotalNum of the block:
ItemPointTotalNum＝(ItemRight–ItemLeft+1)*(ItemBottom–ItemTop+1)。
step (6), according to the RGB color values, calculating a sampling point array DSB for the nth block diagram at a color sampling interval SamplingInterval: DSB { { Ai, Weight i } }; a. theiRepresenting the current sample color value, WeightiRepresenting the current sample color value as AiWhen the color value in the block map is AiThe number of the pixel points;
step (7), counting the color threshold RGBThreshold of the nth block diagram,
circularly sampling point array DSB, accumulating sampling color value A from i-0iStatistic value of (3)iThe loop is skipped until the accumulated value is greater than ItemPointTotalNum RGBThresholdPercent. RGBThreshold ═ (Weight 0+ Weight 1+ … + Weight i)
Step (8), traversing each pixel point of the nth block diagram, if SrcImage [ x ] [ y ] < (RGBThreshold) and DscImage [ x ] [ y ] < 0, otherwise, DscImage [ x ] [ y ] < 255
And (9) calculating each minimap according to the steps (5) to (8) to obtain the final DscImage.
Step (10), externally calculating the value of parameters such as RGBThresholdPercent and the like through parameter quality
RGBThresholdPercent1＝0.6+0.15*quality；
ImageRect1＝700*quality；
SamplingInterval1＝RGBThresholdPercent1*4；
RGBThresholdPercent2＝0.45+0.15*quality；
ImageRect2＝35/quality；
SamplingInterval2＝RGBThresholdPercent2*4；
Step (11), performing steps (2) to (9) by using RGBThresholdPercent1, SamplingInterval1 and ImageRect1 to obtain DscImage 1;
performing steps (2) - (9) with RGBThresholdPercent2, SamplingInterval2, ImageRect2 to obtain DsciImage 2;
and (12) sequentially traversing the pixels of the DscImage1 and DscImage2, wherein if the DscImage1[ x ] [ y ] ═ 255, the DscImage2[ x ] [ y ] ═ 255.
And (13) using the traversed DscImage2 to generate a picture.
The value of n according to the invention is from 0;
the quality of the invention can adjust the image processing effect, the quality is more than 0.0 and less than 1.0, the standard value is 0.8, more noise can be caused when the standard value is less than 0.8, and partial text information can be lost when the standard value is more than 0.8.
Claims (3)
1. A picture ground color bleeding and denoising processing method is characterized by comprising the following steps:
the method comprises the following steps of (1) binarizing an original picture, expressing an R value set in picture pixel RGB values by using SrcImage, expressing the R value of a certain pixel point (x, y) of the picture by using SrcImage [ x ] [ y ], and respectively expressing the width and the height of the picture by using ImageWidth and Imageheight;
step (2), externally providing parameter values: color threshold ratio RGBThresholdPercent, sampling interval SamplingInterval and segmentation graph edge length ImageRect;
step (3), creating a two-dimensional array DscImage for storing R value information of each pixel point after picture processing, wherein the DscImage [ x ] [ y ] and the SrcImage [ x ] [ y ] coordinate correspond to each other one by one;
step (4), carrying out block statistics on the picture according to the ImageRect of the edge length of the segmentation picture;
step (5), acquiring the area information of the nth block diagram;
step (6), according to the RGB color values, calculating a sampling point array DSB for the nth block diagram at a color sampling interval SamplingInterval: DSB { { Ai, Weight i } }; a. theiRepresenting the current sample color value, WeightiRepresenting the current sample color value as AiWhen the color value in the block map is AiThe number of the pixel points;
step (7), counting the color threshold RGBThreshold of the nth block diagram:
circularly sampling point array DSB, accumulating sampling color value A from i-0iStatistic value of (3)iJumping out of the loop until the accumulated value is greater than ItemPointTotalNum RGBThresholdPercent; RGBThreshold ═ (Weight 0+ Weight 1+ … + Weight i)
Step (8), traversing each pixel point of the nth block diagram, if SrcImage [ x ] [ y ] < (RGBThreshold) and DscImage [ x ] [ y ] < 0, otherwise, DscImage [ x ] [ y ] < 255
Step (9), calculating each minimap according to the steps (5) to (8) to obtain the final Dscimage;
step (10), calculating the values of RGBThresholdPercent, ImageRect1 and SamplingInterval parameters through the parameter quality:
RGBThresholdPercent1＝0.6+0.15*quality；
ImageRect1＝700*quality；
SamplingInterval1＝RGBThresholdPercent1*4；
RGBThresholdPercent2＝0.45+0.15*quality；
ImageRect2＝35/quality；
SamplingInterval2＝RGBThresholdPercent2*4；
step (11), performing steps (2) to (9) by using RGBThresholdPercent1, SamplingInterval1 and ImageRect1 to obtain DscImage 1;
performing steps (2) - (9) with RGBThresholdPercent2, SamplingInterval2, ImageRect2 to obtain DsciImage 2;
step (12), sequentially traversing the pixels of the DscImage1 and DscImage2, wherein if DscImage1[ x ] [ y ] is 255, DscImage2[ x ] [ y ] is 255;
and (13) using the traversed DscImage2 to generate a picture.
2. The method for processing under-color-fading and denoising of an image according to claim 1, wherein the block statistics of step (4) is implemented as follows:
the width of the picture is divided into WidthNum parts: (ii) WidthNum ═ ImageWidth/ImageRect + (ImageWidth% ImageRect >01: 0);
the height of the picture is divided into HeightNum parts: height num ═ ImageHeight/ImageRect + (ImageHeight% ImageRect >01: 0);
pictures were divided into ImageNum: ImageNum ═ WidthNum ═ HeightNum.
3. The method as claimed in claim 2, wherein the step (5) of obtaining the region information of the nth block map includes:
the four values of the block are respectively as follows: ItemTop, ItemBottom, ItemLeft, ItemRight:
ItemTop＝n/WidthNum*ImageRect；
ItemBottom ═ ItemTop + ImageRect, ItemBottom ═ ImageHeight if ItemBottom > ImageHeight;
ItemLeft＝n％WidthNum*ImageRect；
ItemRight ═ ItemLeft + ImageRect, ItemRight ═ ImageWidth if ItemRight > ImageWidth;
the total pixel point ItemPointTotalNum of the block:
ItemPointTotalNum＝(ItemRight–ItemLeft+1)*(ItemBottom–ItemTop+1)。
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN101908330A (en) * | 2010-07-26 | 2010-12-08 | 武汉大学 | Method for display equipment with narrow dynamic range to reproduce image with wide dynamic range |
| CN102542528A (en) * | 2011-12-26 | 2012-07-04 | Tcl集团股份有限公司 | Image conversion processing method and system |
| CN107016392A (en) * | 2016-01-27 | 2017-08-04 | 四川效率源信息安全技术股份有限公司 | A kind of method of text border in removal picture |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN101908330A (en) * | 2010-07-26 | 2010-12-08 | 武汉大学 | Method for display equipment with narrow dynamic range to reproduce image with wide dynamic range |
| CN102542528A (en) * | 2011-12-26 | 2012-07-04 | Tcl集团股份有限公司 | Image conversion processing method and system |
| CN107016392A (en) * | 2016-01-27 | 2017-08-04 | 四川效率源信息安全技术股份有限公司 | A kind of method of text border in removal picture |
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Address after: 12 / F, building 8, No. 19, Jugong Road, Xixing street, Binjiang District, Hangzhou City, Zhejiang Province, 310051 Patentee after: ZHEJIANG WANPENG EDUCATION SCIENCE AND TECHNOLOGY STOCK CO.,LTD. Address before: Room 1406, Hangzhou e-commerce building, 118 Wensan West Road, Xihu District, Hangzhou City, Zhejiang Province, 310013 Patentee before: ZHEJIANG WANPENG EDUCATION SCIENCE AND TECHNOLOGY STOCK CO.,LTD. |
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Address after: 12 / F, building 8, No. 19, Jugong Road, Xixing street, Binjiang District, Hangzhou City, Zhejiang Province, 310051 Patentee after: Zhejiang Wanpeng Digital Intelligence Technology Co.,Ltd. Address before: 12 / F, building 8, No. 19, Jugong Road, Xixing street, Binjiang District, Hangzhou City, Zhejiang Province, 310051 Patentee before: ZHEJIANG WANPENG EDUCATION SCIENCE AND TECHNOLOGY STOCK CO.,LTD. |