CN111652808A - Infrared image detail enhancement and noise suppression method - Google Patents
Infrared image detail enhancement and noise suppression method Download PDFInfo
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Abstract
The invention discloses an infrared image detail enhancement and noise suppression method, which is characterized in that a weight value in a larger range is used for processing an image, Gaussian noise in the infrared image is effectively eliminated, certain image details are reserved, and the image quality is effectively improved compared with an original mean filtering image. The original image is subjected to Laplace transform to obtain more detailed information of the image, and the image is fused to obtain an image with multiple image details and low noise. The method has the advantages of simple thought, low calculation complexity and good effect.
Description
Technical Field
The invention belongs to the technical field of infrared image processing, particularly relates to an infrared image edge detail enhancement method capable of suppressing noise, and particularly relates to an infrared image detail enhancement and noise suppression method.
Background
With the continuous development of infrared detection technology, infrared imaging technology is increasingly applied to the fields of military, medical treatment and the like. However, because the infrared image is a gray image, it lacks stereoscopic impression, has low resolution and contrast, strong spatial correlation, blurred image effect, and the influence of the manufacturing process of the infrared detector makes the infrared imaging effect far inferior to the visible light imaging effect.
The infrared image noise mainly includes gaussian noise, salt and pepper noise, composite noise and the like. Because the quality of the infrared image is integrally influenced by various noises, the infrared image noise reduction method is widely concerned, but the existing noise reduction algorithm has certain defects: (1) some noise reduction algorithms can lose image detail information, so that the noise-reduced image loses part of characteristic information; (2) after some noise reduction algorithms are used for processing, the original signal-to-noise ratio is reduced or improved a little, and the image definition degree is improved a little; (3) although some noise reduction algorithms can effectively improve image details, the operation is too complex to facilitate the hardware implementation of the algorithms.
Disclosure of Invention
The present invention is directed to provide a method for enhancing details of an infrared image and suppressing noise, which aims to solve the technical problem of poor infrared image processing effect in the background art.
In order to solve the above technical problem, the present invention provides an infrared image detail enhancement and noise suppression method, which includes the following steps:
(1) obtaining an original infrared image P0；
(2) Calculating the weight value W of all pixel points within the size of 5 × 5 taking the coordinates of the pixel points as the centern；
(3) For the obtained weighted value WnCarrying out normalization to obtain W;
(4) carrying out weighted mean filtering on the gray value of the pixel in a 3 × 3 size area with the pixel point as the center and the weight of the corresponding position of the gray value to obtain a weighted filtered image P1；
(5) For image P0Performing Laplace transform to obtain an image P2Obtaining the strengthened image edge information;
(6) filtering the obtained image P1And picture P2Obtaining an image P by fusing using a Laplacian pyramid transform3And outputs the picture P3；
Wherein the weight WnIs measured in EuropeThe reciprocal of the distance of formula (I) is calculated as:
wherein (x)0，y0) And expressing the position coordinates of the central pixel point, and normalizing the formula:
wherein, the central pixel point (x) is aligned by the obtained weight value0，y0) Carrying out mean value filtering on the gray values of the pixels in the surrounding 3 × 3 size area to obtain a central pixel point (x)0，y0) The gray values of (a) are:
g＝w1×g(i-1，j+1)+w2×g(i，j+1)+w3×g(i+1，j+1)+w4×g(i-1，j)+w5×g(i，j)+w6×g(i+1，j)+w7×g(i-1，j-1)+w8×g(i，j-1)+w9×g(i+1，j-1)
wherein, the laplacian of the image function f (x, y) defines:
wherein
And
comprises the following steps:
the laplace operator is found to be:
wherein the weighted mean filtered image P1And the image P sharpened by the Laplacian operator2Performing image decomposition, performing fusion processing on the obtained images of each layer, and performing image expansion and pyramid reconstruction to obtain a final fusion image P3。
Compared with the prior art, the method based on infrared image detail enhancement and noise suppression provided by the invention has the advantages that the image is processed by using the weight value in a wider range, the Gaussian noise in the infrared image is effectively eliminated, certain image details are reserved, and the image quality is effectively improved compared with the original mean value filtering image. The original image is subjected to Laplace transform to obtain more detailed information of the image, and the image is fused to obtain an image with multiple image details and low noise. The method has the advantages of simple thought, low calculation complexity and good effect.
Drawings
FIG. 1 is a flow chart of the method of the present application;
FIG. 2 is a diagram of a 5 × 5 weighting template of the present application;
FIG. 3 is a 3 × 3 filter template diagram of the present application;
FIG. 4 is a graph of a template for Laplace operator according to the present application;
fig. 5 is a laplacian pyramid fusion block diagram according to the present application.
Detailed Description
The invention is described in further detail below with reference to the figures and specific examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when used in this specification the singular forms "a", "an" and/or "the" include "specify the presence of stated features, steps, operations, elements, or modules, components, and/or combinations thereof, unless the context clearly indicates otherwise.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The invention designs an algorithm for enhancing the details of an infrared image and suppressing noise, which effectively reduces Gaussian noise in the infrared image and simultaneously retains the details of the infrared image and the definition of a target edge in the image as much as possible.
Examples
The embodiment provides a method for enhancing details and suppressing noise of an infrared image, which includes the following specific steps as shown in fig. 1:
(1) obtaining an original infrared image P0；
(2) Calculating the weight value W of all pixel points within the size of 5 × 5 taking the coordinates of the pixel points as the centern(n is an integer of 1 to 25);
(3) for the obtained weighted value WnCarrying out normalization to obtain W;
(4) carrying out weighted mean filtering on the gray value of the pixel in a 3 × 3 size area with the pixel point as the center and the weight of the corresponding position of the gray value to obtain a weighted filtered image P1；
(5) For image P0Performing Laplace transform to obtain an image P2Obtaining the strengthened image edge information;
(6) filtering the obtained image P1And picture P2Obtaining an image P by fusing using a Laplacian pyramid transform3And outputs the picture P3；
Weighting value W in step (2) of the inventionnThe position coordinates (x, y) corresponding to each pixel point within the size of 5 × 5 and the position coordinate (x) of the center pixel point are calculated by using the reciprocal of the Euclidean distance0，y0) The weight template is calculated and found as shown in fig. 2, and the calculation formula is:
for the weight W obtained in step (2)n(x, y) carrying out normalization calculation to obtain a normalized weight value wn：
After the obtained weight is processed to obtain a new weight value, the step (4) obtains the weight value w through the obtained weight valuenCalculating the gray value g (i, j) corresponding to the pixel point in the area with the size of 3 × 3 around the central pixel point (i, j), and obtaining the central pixel point (x) by the filtering template as shown in fig. 30，y0) The gray value g (i, j) of (d) is:
g＝w1×g(i-1，j+1)+w2×g(i，j+1)+w3×g(i+1，j+1)+w4×g(i-1，j)+w5×g(i，j)+w6×g(i+1，j)+w7×g(i-1，j-1)+w8×g(i，j-1)+w9×g(i+1，j-1)
obtaining an image P by processing a new gray value1。
Step 5 the laplace transform through the two-dimensional image function f (x, y) is the isotropic second derivative:
second order difference in two-dimensional image function f (x, y) in x and y directions
And
comprises the following steps:
second order difference of x and y directions
And
substituting into the image function to find the second derivative of the laplacian f (x, y) yields the discrete form of the equation:
using the Laplace operator template shown in FIG. 4, an image P is obtained after Laplace operator processing2The image contains details and edge information of the original image.
As shown in fig. 5, the flow of step (6) is to perform image decomposition on the image P1 subjected to the weighted average filtering and the image P2 subjected to the sharpening by the laplacian operator to obtain a laplacian layer. Treating the obtained P1、P2After fusion processing is carried out on each layer of image, image expansion and pyramid reconstruction are carried out, and a final fusion image P is obtained3The processed image P3 can effectively enhance the details of the image and reduce the noise of the image.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (6)
1. An infrared image detail enhancement and noise suppression method is characterized in that: the method comprises the following steps:
(1) obtaining an original infrared image P0；
(2) Calculating the weight value W of all pixel points within the size of 5 × 5 taking the coordinates of the pixel points as the centern(n is an integer of 1 to 25);
(3) for the obtained weighted value WnCarrying out normalization to obtain W;
(4) carrying out weighted mean filtering on the gray value of the pixel in a 3 x 3 area with the pixel point as the center and the weight value of the corresponding position of the gray value of the pixel to obtain a weighted filtered image P1;
(5) for image P0Performing Laplace transform to obtain an image P2Obtaining the strengthened image edge information;
(6) filtering the obtained image P1And picture P2Obtaining an image P by fusing using a Laplacian pyramid transform3And outputs the picture P3。
2. The method for enhancing details and suppressing noise of infrared image as claimed in claim 1, wherein: the weight value W in the step (2)nThe method is obtained by using the reciprocal calculation of the Euclidean distance, and the calculation formula is as follows:
3. the method for enhancing details and suppressing noise of infrared image as claimed in claim 2, wherein: (x) in the step (2)0，y0) Representing a center pixel pointAnd (3) and normalizing the weights obtained in step (2):
4. the method of claim 3, wherein the method comprises the following steps: processing the obtained weight to obtain a new weight, and aligning the center pixel point (x) with the obtained weight in step (4)0，y0) Carrying out mean value filtering on the gray values of the pixels in the surrounding 3 × 3 size area to obtain a central pixel point (x)0，y0) The gray values of (a) are:
g＝w1×g(i-1,j+1)+w2×g(i,j+1)+w3×g(i+1,j+1)+w4×g(i-1,j)+w5×g(i,j)+w6×g(i+1,j)+w7×g(i-1,j-1)+w8×g(i,j-1)+w9×g(i+1,j-1)
obtaining an image P by processing a new gray value1。
5. The method of claim 4, wherein the method comprises the following steps: in the step (5), the laplacian definition of the image function f (x, y) is as follows:
wherein
And
comprises the following steps:
the laplace operator is found to be:
using a Laplace operator template to obtain an image P after Laplace operator processing2The image contains details and edge information of the original image.
6. The method of claim 5, wherein the method comprises: in the step (6), the image P1 subjected to weight average filtering and the image P2 subjected to laplacian operator sharpening are subjected to image decomposition to obtain a Laplacian layer, a three-layer design is adopted, the Laplacian image at the upper layer is from the down-sampling of the previous layer, and the P obtained by processing is subjected to down-sampling1、P2After fusion processing is carried out on each layer of image, image expansion and pyramid reconstruction are carried out, and a final fusion image P is obtained3The processed image P3 can effectively enhance the details of the image and reduce the noise of the image.
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