[ Huaqiang Security Network News ] At present, many places have large screen splicing screens installed, but there are many splicing screens that are difficult to remove. Here are some ways to analyze the splicing seams:
In general, when image stitching is performed, stitching seams are inevitably generated on the border of the stitching. This is because the nuances of the two images to be stitched in the gray scale will result in a noticeable stitching. In the actual imaging process, this nuance is hard to avoid. Therefore, the difficulty of image mosaic technology is to accurately find the positional relationship between images, and smoothly connect two or more images together to obtain a global image. The basic idea of ​​this paper is to break through the previous search for stitching lines, as long as you find a best stitching point, use this point as a straight line as the irrationality of the stitching line, but take a closed value and find each in the closed value range. For the splicing points, the broken lines connecting these points are used as splicing lines for splicing.
Method Seam eliminate seams conventional method of eliminating the fight, many of them used more methods; median filter to eliminate stitching, using a wavelet transform method, a weighted average method, median filter The slit median filtering method is a median filtering of the area near the seam. The pixel having a relatively large difference from the surrounding gradation value takes a value close to the surrounding pixel, thereby eliminating the discontinuity of the light intensity. The median filter processes the strips around the seam. The method is fast, but the quality is average. The smooth result will reduce the resolution of the image, making the image details inconsistible and producing image blur.
2. Using wavelet transform to eliminate stitching seams Wavelet transform method is also a commonly used method. He makes full use of the multi-resolution characteristics of wavelet transform to solve the seam problem of stitched images. The principle is: since the wavelet transform has the property of a band-pass filter, the wavelet transform component at different scales actually occupies a certain bandwidth. The larger the scale j, the higher the frequency of the component, so each wavelet component is The bandwidth is small, and the two images to be spliced ​​are first decomposed into wavelet components of different frequencies according to the wavelet decomposition method. As long as the decomposition is sufficiently thin, the bandwidth of the wavelet component can be sufficiently small. Then, at different scales, different stitching widths are selected, and the two images are first spelled by wavelet components at different scales, and then restored to the entire image by the recovery procedure. The image thus obtained can well meet the requirements of both clarity and smoothness. However, wavelet transform also has shortcomings. For example, the wavelet transform algorithm is more complicated, and it needs to be spliced ​​first in the wavelet transform domain. In the calculation process, a large number of floating-point operations and boundary processing problems are involved, and large-capacity images in actual production are needed. When processing, the computer has a large memory overhead, and the processing time is long, and the splicing speed is slow.
3. Using the method of weighted smoothing to eliminate the stitching seam In practice, more methods are used, and there is a method of weighting and smoothing the overlapping area. The idea of ​​this method is that the gray value Pixel of the pixel points in the overlapping area of ​​the image is obtained by weighted averaging of the gray values ​​LPixel and RPixel of the corresponding points in the two images, namely:
OPixel-kXLPixel+(l-k)XRPixel where: k is a gradation factor that satisfies the conditions:
When searching for the best stitching line, a sliding window is used to select the pixel with the smallest difference in gray value row by row as the best stitching point on the overlapping area of ​​the image. However, according to this splicing point selection method, a new problem arises, that is, there is often a phenomenon in which the positions of the upper and lower splicing points are far apart, so that sometimes the splicing is sometimes caused by a large difference in gradation between the up and down lines. seam. In order to avoid such phenomena, it is necessary to consider not only the difference in gray value of adjacent splice points, but also the position of adjacent splice points. This introduces a threshold T that limits the range of the best splice points to this threshold. Except that the first line is processed according to the principle that the difference of the gray value is the smallest, the splicing points of the other lines are selected from a selected area: the point in the same column as the selected splicing point of the previous line and the width of the left and right centered on the point is T. The point in the area. Pick an optimal splice point in this area. After selecting the splicing points of each line and connecting them into a splicing line, it is conceivable that this splicing line may be a splicing line. In this way, since each line selects the point where the difference in the gray level in the neighborhood is the smallest, the joint phenomenon will be greatly improved. At the same time, the value of T cannot be selected too much, and it should be chosen between 1 and 5. After finding the best stitching seam, the overlapping area is further transformed according to the weighting smoothing described above, and the image quality obtained is greatly improved.
In general, when image stitching is performed, stitching seams are inevitably generated on the border of the stitching. This is because the nuances of the two images to be stitched in the gray scale will result in a noticeable stitching. In the actual imaging process, this nuance is hard to avoid. Therefore, the difficulty of image mosaic technology is to accurately find the positional relationship between images, and smoothly connect two or more images together to obtain a global image. The basic idea of ​​this paper is to break through the previous search for stitching lines, as long as you find a best stitching point, use this point as a straight line as the irrationality of the stitching line, but take a closed value and find each in the closed value range. For the splicing points, the broken lines connecting these points are used as splicing lines for splicing.
2. Using wavelet transform to eliminate stitching seams Wavelet transform method is also a commonly used method. He makes full use of the multi-resolution characteristics of wavelet transform to solve the seam problem of stitched images. The principle is: since the wavelet transform has the property of a band-pass filter, the wavelet transform component at different scales actually occupies a certain bandwidth. The larger the scale j, the higher the frequency of the component, so each wavelet component is The bandwidth is small, and the two images to be spliced ​​are first decomposed into wavelet components of different frequencies according to the wavelet decomposition method. As long as the decomposition is sufficiently thin, the bandwidth of the wavelet component can be sufficiently small. Then, at different scales, different stitching widths are selected, and the two images are first spelled by wavelet components at different scales, and then restored to the entire image by the recovery procedure. The image thus obtained can well meet the requirements of both clarity and smoothness. However, wavelet transform also has shortcomings. For example, the wavelet transform algorithm is more complicated, and it needs to be spliced ​​first in the wavelet transform domain. In the calculation process, a large number of floating-point operations and boundary processing problems are involved, and large-capacity images in actual production are needed. When processing, the computer has a large memory overhead, and the processing time is long, and the splicing speed is slow.
3. Using the method of weighted smoothing to eliminate the stitching seam In practice, more methods are used, and there is a method of weighting and smoothing the overlapping area. The idea of ​​this method is that the gray value Pixel of the pixel points in the overlapping area of ​​the image is obtained by weighted averaging of the gray values ​​LPixel and RPixel of the corresponding points in the two images, namely:
OPixel-kXLPixel+(l-k)XRPixel where: k is a gradation factor that satisfies the conditions:
When searching for the best stitching line, a sliding window is used to select the pixel with the smallest difference in gray value row by row as the best stitching point on the overlapping area of ​​the image. However, according to this splicing point selection method, a new problem arises, that is, there is often a phenomenon in which the positions of the upper and lower splicing points are far apart, so that sometimes the splicing is sometimes caused by a large difference in gradation between the up and down lines. seam. In order to avoid such phenomena, it is necessary to consider not only the difference in gray value of adjacent splice points, but also the position of adjacent splice points. This introduces a threshold T that limits the range of the best splice points to this threshold. Except that the first line is processed according to the principle that the difference of the gray value is the smallest, the splicing points of the other lines are selected from a selected area: the point in the same column as the selected splicing point of the previous line and the width of the left and right centered on the point is T. The point in the area. Pick an optimal splice point in this area. After selecting the splicing points of each line and connecting them into a splicing line, it is conceivable that this splicing line may be a splicing line. In this way, since each line selects the point where the difference in the gray level in the neighborhood is the smallest, the joint phenomenon will be greatly improved. At the same time, the value of T cannot be selected too much, and it should be chosen between 1 and 5. After finding the best stitching seam, the overlapping area is further transformed according to the weighting smoothing described above, and the image quality obtained is greatly improved.
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