CN111652808A - A method of infrared image detail enhancement and noise suppression - Google Patents

Abstract

The invention discloses an infrared image detail enhancement and noise suppression method, which uses a wider range of weight values to process the image, effectively eliminates Gaussian noise in the infrared image, and retains certain image details, which is compared with the original average value. Filtering the image effectively improves the quality of the image. By using Laplace transform on the original image, more detailed information of the image can be obtained, and by fusing the images, an image with multiple image details and low noise can be obtained. The idea of the invention is simple, the calculation complexity is not high, and the effect is good.

Description

A method of infrared image detail enhancement and noise suppression

technical field

The invention belongs to the technical field of infrared image processing, in particular to an infrared image edge detail enhancement method with noise suppression, in particular to an infrared image detail enhancement and noise suppression method.

Background technique

With the continuous development of infrared detection technology, infrared imaging technology is increasingly used in military and medical fields. However, because the infrared image is a grayscale image, it lacks stereoscopic effect, 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.

Infrared image noise is mainly Gaussian noise, salt and pepper noise and composite noise. Because the quality of infrared images is the overall impact of a variety of noises, infrared image noise reduction methods have received extensive attention, but the existing noise reduction algorithms have certain defects: (1) Some noise reduction algorithms will lose Image detail information, so that the image after noise reduction loses part of the feature information; (2) Some noise reduction algorithms reduce or improve the original signal-to-noise ratio after processing, and the image clarity is not improved much; (3) Some reduce Although the noise algorithm can effectively improve the image details, its operation is too complicated, which is not conducive to the hardware implementation of the algorithm.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide an infrared image detail enhancement and noise suppression method in view of the technical problem that the infrared image processing effect is not good in the above-mentioned background art.

In order to solve the above technical problems, the present invention provides a method for enhancing infrared image details and suppressing noise, comprising the following steps:

(1) Obtain the original infrared image P ₀ ;

(2) Calculate the weight value W _n of all pixel points in the size of 5×5 with the pixel point coordinates as the center;

(3) Normalize the obtained weight value W _n to obtain W;

(4) performing weighted mean filtering with the weight of the pixel gray value and its corresponding position in a 3×3 area with the pixel as the center to obtain a weighted filtered image P ₁ ;

(5) Laplace transform is performed on the image P ₀ to obtain the image P ₂ , and the enhanced image edge information is obtained;

(6) fuse the filtered image P ₁ and the image P ₂ by using the Laplace pyramid transform to obtain the image P ₃ and output the image P ₃ ;

Wherein, the value of the weight W _n adopts the reciprocal of the Euclidean distance, and its calculation formula is:

Among them, (x ₀ , y ₀ ) represents the position coordinates of the center pixel, and the above formula is normalized:

Among them, average filtering is performed on the gray value of the pixels in the 3×3 area around the central pixel (x ₀ , y ₀ ) through the obtained weight value, and the gray value of the central pixel (x ₀ , y ₀ ) is obtained. :

g=w ₁ ×g(i-1,j+1)+w ₂ ×g(i,j+1)+w ₃ ×g(i+1,j+1)+w ₄ ×g(i-1 , j)+w ₅ ×g(i,j)+w ₆ ×g(i+1,j)+w ₇ ×g(i-1,j-1)+w ₈ ×g(i,j-1 )+w ₉ ×g(i+1, j-1)

Among them, it is defined by the Laplace operator of the image function f(x, y):

和

为：in

and

for:

The Laplace operator is obtained as:

Among them, the image P ₁ filtered by the weighted mean value and the image P ₂ sharpened by the Laplacian operator are decomposed, and the obtained images of each layer are fused and then the image is expanded and the pyramid is reconstructed. The final fused image P ₃ is obtained.

Compared with the prior art, the present invention proposes a method based on infrared image detail enhancement and noise suppression, which uses a wider range of weight values to process the image, effectively eliminates the Gaussian noise in the infrared image, and retains a certain amount of noise. Compared with the original mean-filtered image, the image quality is effectively improved. By using Laplace transform on the original image, more detailed information of the image can be obtained, and by fusing the images, an image with multiple image details and low noise can be obtained. The idea of the invention is simple, the calculation complexity is not high, and the effect is good.

Description of drawings

Fig. 1 shows the method flow chart of this application;

FIG. 2 shows a 5×5 weight template diagram of the present application;

FIG. 3 shows a 3×3 filtering template diagram of the present application;

Figure 4 shows the Laplacian operator template diagram of the present application;

FIG. 5 shows a block diagram of the Laplacian pyramid fusion of the present application.

Detailed ways

The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

It should be noted that the terminology used herein is for the purpose of describing specific embodiments only, and is not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural as well, furthermore, it should also be understood that when used in this specification of "comprising" and/or "comprising", it indicates There are features, steps, operations, components or modules, components and/or combinations thereof.

It should be noted that the terms "first", "second", etc. in the description and claims of the present application and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific sequence or sequence. It is to be understood that data so used may be interchanged under appropriate circumstances such that the embodiments of the application described herein can, for example, be practiced in sequences other than those illustrated or described herein. Furthermore, the terms "comprising" and "having" and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those expressly listed Rather, those steps or units may include other steps or units not expressly listed or inherent to these processes, methods, products or devices.

It should be noted that the embodiments in the present application and the features of the embodiments may be combined with each other in the case of no conflict.

The present invention designs an infrared image detail enhancement and noise suppression algorithm, which effectively reduces the Gaussian noise in the infrared image and preserves as many details of the infrared image and sharpness of the target edge in the image as possible.

Example

This embodiment proposes a method for enhancing infrared image details and suppressing noise. The specific steps of the method are shown in FIG. 1 , including:

(1) Obtain the original infrared image P ₀ ;

(2) Calculate the weight value W _n (the value of n is an integer between 1 and 25) of all the pixel points in the size of 5 × 5 with the coordinates of the pixel point as the center;

(3) Normalize the obtained weight value W _n to obtain W;

(4) performing weighted mean filtering with the weight of the pixel gray value and its corresponding position in a 3×3 area with the pixel as the center to obtain a weighted filtered image P ₁ ;

(5) Laplace transform is performed on the image P ₀ to obtain the image P ₂ , and the enhanced image edge information is obtained;

(6) fuse the filtered image P ₁ and the image P ₂ by using the Laplace pyramid transform to obtain the image P ₃ and output the image P ₃ ;

In step (2) of the present invention, the weight value W _n is calculated by using the reciprocal of the Euclidean distance to obtain the position coordinates (x, y) corresponding to each pixel in the size of 5×5 and the position coordinates (x ₀ , y ₀ of the center pixel) ) is calculated, the weight template is shown in Figure 2, and its calculation formula is:

The normalized calculation is performed on the weight W _n (x, y) obtained in step (2) to obtain the normalized weight value w _n :

After the obtained weight is processed to obtain a new weight value, step (4) uses the obtained weight value w _n to calculate the gray value g ( i, j) is calculated, the filtering template is shown in Figure 3, and the gray value g(i, j) of the central pixel point (x ₀ , y ₀ ) is obtained as:

g=w ₁ ×g(i-1,j+1)+w ₂ ×g(i,j+1)+w ₃ ×g(i+1,j+1)+w ₄ ×g(i-1 , j)+w ₅ ×g(i,j)+w ₆ ×g(i+1,j)+w ₇ ×g(i-1,j-1)+w ₈ ×g(i,j-1 )+w ₉ ×g(i+1, j-1)

The image P ₁ is obtained by the new gray value after processing.

Step 5 The Laplace transform of the two-dimensional image function f(x, y) is the isotropic second derivative:

和

为：In the two-dimensional image function f(x, y), the second-order difference of the two directions of x and y

and

for:

和

带入上述图像函数求得拉普拉斯算子f(x，y)的二阶导数中得到上述方程的离散形式：Take the second difference in the x and y directions

and

Bring in the above image function to obtain the second derivative of the Laplace operator f(x, y) to obtain the discrete form of the above equation:

Using the Laplacian operator template as shown in FIG. 4 , an image P ₂ is obtained after being processed by the Laplacian operator, and the image contains the details and edge information of the original image.

The process of step (6) is shown in FIG. 5 , the image P1 filtered by the weighted mean value and the image P2 sharpened by the Laplacian operator are decomposed to obtain the Laplacian layer. In the present invention, three layers are used. By design, the high-level Laplacian images are all down-sampled from the previous layer. The processed images of layers P ₁ and P ₂ are fused, and then image expansion and pyramid reconstruction are performed to obtain the final fused image P ₃ . The processed image P 3 can effectively enhance image details and reduce image quality. noise.

The above are only the preferred embodiments of the present invention. It should be noted that, for those skilled in the art, without departing from the principles of the present invention, several improvements and modifications can be made. These improvements and Retouching 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 center_n(n is an integer of 1 to 25);

(3) for the obtained weighted value W_nCarrying 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.

Images