CN1837854A - Tone Adjustment Method Based on Image Overlapping Area in Remote Sensing Image Mosaic - Google Patents

Abstract

This invention relates to a tone adjustment method based on image overlap region for mosaic of remote sensing image, which comprises the following steps: 1. acquiring the pixel brightness values from the basic image and target image; 2. computing the current bright value and the difference distribution compared with the values from step 1, smoothing the histogram in result, and obtaining the opposite diff as difference value on the peak; 3. stating the pixel brightness value less than one, smoothing the result, and obtaining Pos as the opposite brightness value on the peak; 4. regulating the image color by the Pos value. This invention reduces algorithm complexity, and improves practicability.

Description

Tone Adjustment Method Based on Image Overlapping Area in Remote Sensing Image Mosaic

Technical field:

The invention belongs to the field of remote sensing image mosaic technology, and relates to a method for adjusting hue in remote sensing image mosaic based on the difference in brightness values of overlapping regions of adjacent images.

Background technique:

Remote sensing image mosaicking is one of the important contents of remote sensing technology application, especially for large-scale satellite remote sensing and regional aerial remote sensing, it often requires multiple images to be mosaiced; Features with the same name appear in different tones on adjacent images. If no countermeasures are taken for these situations, the overall mosaic image will have obvious brightness unevenness and color imbalance. This phenomenon leads to the fact that the spliced image is not conducive to human observation, and also affects the analysis and recognition of ground object images and professional information. Therefore, in the process of image mosaic, a technology is needed to adjust the mosaic image. overall tone.

The current general tone adjustment methods can be summarized as: histogram method, variance mean method, and image information entropy method; the common feature of these methods is that the calculation amount is large, which often affects the use efficiency. This disadvantage is especially noticeable during processing.

Invention content:

The purpose of the present invention is to provide a tone adjustment method in remote sensing image mosaic based on the difference of brightness values in overlapping regions of adjacent images with simple algorithm and easy implementation.

The technical solution provided by the present invention is: a tone adjustment method based on image overlapping regions in remote sensing image mosaic, comprising the following steps:

1. Obtain the brightness value of the reference image and the overlapped part of the processing image from the reference image; the processing steps are as follows:

I, according to the geodetic coordinates of the reference image and the image to be processed, determine the region that overlaps with the image to be processed in the reference image;

II, take out the brightness value g(x, y) of the pixels in the region obtained by step I in the reference image;

2. Statistically analyze the distribution of the difference between the luminance value of the image to be processed and the luminance value obtained in step 1 in the overlapping area, and smooth the histogram of the statistical result to obtain the difference diff corresponding to the maximum peak of the histogram; the processing steps are as follows:

I. Determine the overlapping area of the image to be processed and the image to be processed according to the geodetic coordinates of the reference image and the image to be processed;

II, take out the brightness value f(x, y) of the pixel in the region obtained by step 2 and I in the image to be processed;

III, the histogram distribution S[i] of statistical brightness difference g(x, y)-f(x, y);

IV. Smooth brightness difference histogram S[i]:

S[min]=(S[min]+S[min+1])/2

S[n]=(S[n-1]+S[n]+S[n+1])/3

S[max]=(S[max]+S[max-1])/2

Among them, min is the minimum brightness difference, max is the maximum brightness difference, and n takes the value between min and max, that is, min<n<max

V. Record the brightness difference diffi when S[i] is the largest;

3. Statistics on the distribution of the brightness values of the pixels whose brightness value difference is equal to diff on the overlapping area of the image to be processed, smoothing the histogram of the statistical results, and obtaining the brightness value Pos corresponding to the maximum peak of the histogram; the processing steps are as follows:

1, the luminance histogram distribution situation F [j] of f (x, y) when the luminance difference g (x, y)-f (x, y)=diff in the overlapping region on the statistical image to be processed;

II. Smooth the histogram F[j] with the method used in step 2 and IV, and record the brightness value Pos when F[j] is maximum after smoothing;

4. Adjust the overall tone of the image T(x, y) to be processed:

When T(x, y)<Pos: T(x, y)=T(x, y)*(Pos+diff)/Pos

When T(x, y)>Pos: T(x, y)=[T(x, y)-Pos]*[255-(Pos+diff)]/(255-Pos)+(Pos+diff);

In the formula:

x is the abscissa value of the pixel in the image, and the value is 1, 2, 3, ..., m; where m is the number of pixels in the horizontal direction of the image;

y is the ordinate value of the pixel in the image, and the value is 1, 2, 3, ..., n; where n is the number of pixels in the vertical direction of the image;

T(x, y) is the brightness value of the pixel at (x, y) in this image.

When the image to be processed is a color image, perform the following processing before step 1 above:

A. According to the conversion method from the RGB color space to the HIS double cone model color space, the RGB value of each pixel of the image to be processed is converted into an HSI value;

B. The I component value of the HIS obtained after RGB to HIS conversion of each pixel is proposed as the brightness value.

When the image to be processed is multiple images, each processed image is spliced with the reference image to form a new reference image, and the pixel values of the overlapping area adopt the pixel values of the overlapping area of the original reference image.

The present invention can adjust the overall tone difference of the image. The invention utilizes the statistical characteristics of the brightness difference value histogram in the overlapping area of the image to adjust the overall tone of the image, reduces the complexity of the algorithm, has simple algorithm, is easy to implement, and improves practicability.

Description of drawings:

Accompanying drawing is the schematic flow chart of the present invention.

Detailed ways:

Referring to accompanying drawing, the present invention comprises the following steps:

1. RGB-HIS transformation

According to the knowledge of chromaticity, the human eye is more sensitive to brightness. At the same time, since the difference in hue of remote sensing images is mainly caused by uneven illumination, the difference in hue is not obvious. Therefore, all images are converted from RGB color space to the HIS chromaticity space for processing; the formula is:

$\mathrm{<span\; class="notranslate">\; \&theta;</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; arccos</span>}<span\; class="notranslate">\; \{</span>\frac{\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; [</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; G</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ]</span>}{{<span\; class="notranslate">\; [</span>{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; G</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; G</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ]</span>}^{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; \}</span>$

$\mathrm{<span\; class="notranslate">\; S</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>$ $\frac{\mathrm{<span\; class="notranslate">\; 3</span>}}{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; G</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; )</span>}$ $<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; min</span>}$ $<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; G</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; )</span>$ $<span\; class="notranslate">\; ]</span>$

$\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; G</span>}<span\; class="notranslate">\; )</span>$

In the formula:

R is the red component value of each pixel of the color image;

G is the green component value of each pixel of the color image;

B is the blue component value of each pixel of the color image.

2. Obtain the brightness value of the overlapping area of the image

1. Take out the coordinate information of the image to be processed, and take out the area that overlaps with it in the reference image;

II. Take out the brightness value of the pixels in the overlapping area of the reference image and the image to be processed:

3. Overall tone adjustment

1. Set up a two-dimensional matrix B (x, y) with the same size as the image to be processed, and mark the part of the matrix corresponding to the overlapping area of the image to be processed;

II. In the marked image overlapping area, the current image brightness value is expressed as f(x, y), and the brightness value obtained in step 2 can be expressed as g(x, y);

III. Statistics of the histogram distribution S[i] of the brightness difference g(x, y)-f(x, y) in the overlapping area, S[i] can be obtained by traversing the pixels in the overlapping area:

S[i]=S[i]+1 (i=g(x,y)-f(x,y))

In the formula: x, y are the row and column numbers of the pixels in the overlapping area

IV. Smooth brightness difference histogram S[i]:

S[min]=(S[min]+S[min+1])/2

S[n]=(S[n-1]+S[n]+S[n+1])/3

S[max]=(S[max]+S[max-1])/2

Among them, min is the minimum brightness difference, max is the maximum brightness difference, and n takes the value between min and max, that is, min<n<max;

V. Record the brightness difference diff when S[i] is the largest;

VI, the luminance histogram distribution situation F [j] of f (x, y) when the luminance difference g (x, y)-f (x, y)=diff in the overlapping area on the statistical image to be processed;

VII, smoothing the histogram F[j] with the same method used in the fourth step, and recording the brightness value Pos when F[j] is maximum after smoothing;

VIII. Adjust the overall tone of the image T(x, y) to be processed:

When T(x, y)<Pos: T(x, y)=T(x, y)*(Pos+diff)/Pos

When T(x, y)>Pos: T(x, y)=[T(x, y)-Pos]*[255-(Pos+diff)]/(255-Pos)+(Pos+diff);

x is the abscissa value of the pixel in the image, and the value is 1, 2, 3, ..., m; where m is the number of pixels in the horizontal direction of the image;

y is the ordinate value of the pixel in the image, and the value is 1, 2, 3, ..., n; where n is the number of pixels in the vertical direction of the image;

T(x, y) is the brightness value of the pixel at (x, y) in this image;

4. Splicing the processed image and the reference image as the reference image for the next processing:

I, can establish a circumscribed polygon of these two images according to the coordinates of the processed image and the reference image;

II, the polygon obtained by step I can set up a blank image corresponding to the size of this polygon area;

III. Assign the pixel value of the corresponding pixel in the reference image to the pixels in the region overlapping the reference image in the blank image;

IV, assign the pixel point that is not assigned a value by the step III operation in this blank image to the pixel value of the corresponding pixel on the image after processing;

V. After the above steps, a new reference image can be obtained, and then return to the second step to cycle until all images are processed.

Claims (3)

1, the hue adjusting method based on image overlap area during a kind of remote sensing image is inlayed is characterized in that: may further comprise the steps:

One, obtain reference images and handle image overlap brightness value partly from reference images:

The terrestrial coordinate of I, benchmark image and pending image determines with pending image overlapping areas is arranged in the reference images;

II, take out in the reference images by the brightness value g of the pixel in the resulting zone of step I (x, y);

Two, the difference distribution situation of the brightness value that pending brightness value of image and step 1 obtain on the statistics overlapping region is made smoothing processing to the statistics histogram, obtains the difference diff of histogram peak-peak place correspondence:

The terrestrial coordinate of I, benchmark image and pending image is determined pending image and reference images overlapping areas;

H, take out in the pending image by the brightness value f of the pixel in step 2, the resulting zone of I (x, y);

III, statistics luminance difference g (x, y)-f (x, histogram distribution situation S[i y)];

IV, level and smooth luminance difference histogram S[i]:

S[min]＝(S[min]+S[min+1])/2

S[n]＝(S[n-1]+S[n]+S[n+1])/3

S[max]＝(S[max]+S[max-1])/2

Wherein, min is the minimum brightness difference, and max is a maximum luminance difference, and n gets the value between min and the max,

Be min＜n＜max;

V, record S[i] luminance difference diff when maximum;

Three, the brightness value difference equals the distribution situation of the pixel brightness value of diff on the pending image overlap area of statistics, and the statistics histogram is made smoothing processing, obtains the brightness value Pos of histogram peak-peak place correspondence:

Work as in the overlapping region on I, the pending image of statistics luminance difference g (x, y)-f (x, y)=f during diff (x, brightness histogram distribution situation F[j y)];

II, with the used method of step 2, IV to histogram F[j] carry out smoothing processing, and the level and smooth back of record F[j] brightness value Pos when maximum;

Four, with pending image T (x, y) do adjustment on the integral color:

T (x, y)＜during Pos: T (x, y)=T (x, y) * (Pos+diff)/Pos;

T (x, y)＞during Pos: T (x, y)=[T (x, y)-Pos] * [255-(Pos+diff)]/(255-Pos)+(Pos+diff);

In the formula:

X is the abscissa value of pixel in the image, value is 1,2,3 ..., m; The m number of pixels on the horizontal direction of image for this reason wherein;

Y is the ordinate value of pixel in the image, value is 1,2,3 ..., n; The n number of pixels on the vertical direction of image for this reason wherein;

T (x, y) for this reason in the image position at (x, y) brightness value of the pixel on.

2, method according to claim 1 is characterized in that: when processed image is chromatic image, carried out following processing earlier before step 1:

A. according to the RGB color space to the conversion method of the HIS double cone model color space, the rgb value of pending each pixel of image is converted to the HSI value;

B. the I component value of the HIS that obtains after the HIS conversion through RGB that proposes each pixel is as brightness value.

3, method according to claim 1 and 2, it is characterized in that: when processed image is several images, image and reference images after handling are spliced to form new reference images at every turn, and the overlapping region pixel value adopts the pixel value of former benchmark image overlap area.