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

A method of infrared image detail enhancement and noise suppression Download PDF

Info

Publication number
CN111652808A
CN111652808A CN201910689889.1A CN201910689889A CN111652808A CN 111652808 A CN111652808 A CN 111652808A CN 201910689889 A CN201910689889 A CN 201910689889A CN 111652808 A CN111652808 A CN 111652808A
Authority
CN
China
Prior art keywords
image
weight
pixel
noise
value
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201910689889.1A
Other languages
Chinese (zh)
Inventor
赵毅强
李体明
林元琦
王品权
李松
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tianjin University
Original Assignee
Tianjin University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Tianjin University filed Critical Tianjin University
Priority to CN201910689889.1A priority Critical patent/CN111652808A/en
Publication of CN111652808A publication Critical patent/CN111652808A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00Image enhancement or restoration
    • G06T5/70Denoising; Smoothing
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00Image enhancement or restoration
    • G06T5/77Retouching; Inpainting; Scratch removal
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10048Infrared image
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20016Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20024Filtering details
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20048Transform domain processing
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20212Image combination
    • G06T2207/20221Image fusion; Image merging

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Image Processing (AREA)

Abstract

本发明公开了一种红外图像细节增强及噪声抑制方法,使用更大范围内的权重值对图像进行处理,有效的消除红外图像中的高斯噪声,并且保留一定的图像细节,对比于原本的均值滤波图像有效的提高了图像的质量。对原始图像通过使用拉普拉斯变换,得到图像较多的细节信息,经过对图像进行融合可以得到一副具有多图像细节低噪声的图像。本发明的思路简单,计算复杂度不高,且效果良好。

Figure 201910689889

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.

Figure 201910689889

Description

一种红外图像细节增强及噪声抑制方法A method of infrared image detail enhancement and noise suppression

技术领域technical field

本发明属于红外图像处理技术领域,特别涉及一种具有抑制噪声的红外图像边缘细节增强方法,具体涉及一种红外图像细节增强及噪声抑制方法。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.

背景技术Background technique

随着红外探测技术的不断发展,红外成像技术被越来越多的应用于军事和医疗等领域。但在因为红外图像是灰度图像,因此其缺乏立体感,分辨度和对比度低、空间相关性强,图像效果模糊,并且红外探测器的制造工艺的影响使得红外成像效果远不如可见光成像效果。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.

红外图像噪声主要是高斯噪声、椒盐噪声及复合噪声等。因为红外图像质量是由多种噪声综合产生的整体影响,因此红外图像降噪方法受到了广泛的关注,但现有的降噪算法存在着一定的缺陷:(1)有的降噪算法会损失图像细节信息,使得降噪后的图像丢失部分特征信息;(2)有的降噪算法处理后使得原信噪比降低或提升不大,对图像清晰程度提升不大;(3)有的降噪算法虽可以有效提升图像细节,但其运算过于复杂不利于算法的硬件实现。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.

发明内容SUMMARY OF THE INVENTION

本发明的目的在于针对上述背景技术中存在的对红外图像处理效果不佳的技术问题,提供一种红外图像细节增强及噪声抑制方法。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.

为了解决上述技术问题,本发明提供一种红外图像细节增强及噪声抑制方法,包括以下步骤: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)获取原始红外图像P0(1) Obtain the original infrared image P 0 ;

(2)计算以像素点坐标为中心5×5大小内全部像素点的权重值Wn(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)对所求得的权重值Wn进行归一化得到W;(3) Normalize the obtained weight value W n to obtain W;

(4)以像素点为中心3×3大小区域内像素灰度值与其对应位置的权重进行加权均值滤波得到经过加权滤波的图像P1(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)对图像P0进行拉普拉斯变换得到图像P2,得到强化后的图像边缘信息;(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)将经过滤波后得到的图像P1与图像P2通过使用拉普拉斯金字塔变换进行融合得到图像P3并输出图像P3(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 ;

其中,所述权重Wn的值采用欧式距离的倒数,其计算公式为:Wherein, the value of the weight W n adopts the reciprocal of the Euclidean distance, and its calculation formula is:

Figure BDA0002147558900000021
Figure BDA0002147558900000021

其中,(x0,y0)表示中心像素点的位置坐标,并对上式进行归一化:Among them, (x 0 , y 0 ) represents the position coordinates of the center pixel, and the above formula is normalized:

Figure BDA0002147558900000022
Figure BDA0002147558900000022

其中,通过求得的权重值对中心像素点(x0,y0)周围3×3大小区域内像素灰度值进行均值滤波,得到中心像素点(x0,y0)的灰度值为: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=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)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)

其中,通过图像函数f(x,y)的拉普拉斯算子定义:Among them, it is defined by the Laplace operator of the image function f(x, y):

Figure BDA0002147558900000031
Figure BDA0002147558900000031

其中

Figure BDA0002147558900000032
Figure BDA0002147558900000033
为:in
Figure BDA0002147558900000032
and
Figure BDA0002147558900000033
for:

Figure BDA0002147558900000034
Figure BDA0002147558900000034

求得拉普拉斯算子为:The Laplace operator is obtained as:

Figure BDA0002147558900000035
Figure BDA0002147558900000035

其中,将经过权重均值滤波的图像P1与经过拉普拉斯算子锐化后的图像P2进行图像分解,将得到的各层图像进行融合处理后进行图像的扩展与金字塔的重构从而得到最终的融合图像P3Among 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.

与现有技术相比,本发明提出了一种基于红外图像细节增强及噪声抑制的方法,使用更大范围内的权重值对图像进行处理,有效的消除红外图像中的高斯噪声,并且保留一定的图像细节,对比于原本的均值滤波图像有效的提高了图像的质量。对原始图像通过使用拉普拉斯变换,得到图像较多的细节信息,经过对图像进行融合可以得到一副具有多图像细节低噪声的图像。本发明的思路简单,计算复杂度不高,且效果良好。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.

附图说明Description of drawings

图1所示为本申请的方法流程图;Fig. 1 shows the method flow chart of this application;

图2所示为本申请的5×5权重模板图;FIG. 2 shows a 5×5 weight template diagram of the present application;

图3所示为本申请的3×3滤波模板图;FIG. 3 shows a 3×3 filtering template diagram of the present application;

图4所示为本申请拉普拉斯算子模板图;Figure 4 shows the Laplacian operator template diagram of the present application;

图5所示为本申请拉普拉斯金字塔融合框图。FIG. 5 shows a block diagram of the Laplacian pyramid fusion of the present application.

具体实施方式Detailed ways

以下结合附图和具体实施例对本发明作进一步详细说明。应当理解,此处所描述的具体实施例仅仅用以解释本发明,并不用于限定本发明。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.

需要注意的是,这里所使用的术语仅是为了描述具体实施方式,而非意图限制根据本申请的示例性实施方式。如在这里所使用的,除非上下文另外明确指出,否则单数形式也意图包括复数形式,此外,还应当理解的是,当在本说明书中使用属于“包含”和/或“包括”时,其指明存在特征、步骤、操作、部件或者模块、组件和/或它们的组合。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.

需要说明的是,本申请的说明书和权利要求书及上述附图中的术语“第一”、“第二”等是用于区别类似的对象,而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换,以便这里描述的本申请的实施方式例如能够以除了在这里图示或描述的那些以外的顺序实施。此外,术语“包括”和“具有”以及他们的任何变形,意图在于覆盖不排他的包含,例如,包含了一系列步骤或单元的过程、方法、系统、产品或设备不必限于清楚地列出的那些步骤或单元,而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。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.

需要说明的是,在不冲突的情况下,本申请中的实施例及实施例中的特征可以相互组合。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.

本发明设计一种应用红外图像细节增强及噪声抑制算法,在有效的降低红外图像中的高斯噪声的同时尽可能多的保留红外图像的细节及图像中目标边缘的清晰度。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.

实施例Example

本实施例提出一种红外图像细节增强及噪声抑制方法,该方法的具体步骤如图1所示,包括: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)获取原始红外图像P0(1) Obtain the original infrared image P 0 ;

(2)计算以像素点坐标为中心5×5大小内全部像素点的权重值Wn(n的值为1到25间的整数);(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)对所求得的权重值Wn进行归一化得到W;(3) Normalize the obtained weight value W n to obtain W;

(4)以像素点为中心3×3大小区域内像素灰度值与其对应位置的权重进行加权均值滤波得到经过加权滤波的图像P1(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)对图像P0进行拉普拉斯变换得到图像P2,得到强化后的图像边缘信息;(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)将经过滤波后得到的图像P1与图像P2通过使用拉普拉斯金字塔变换进行融合得到图像P3并输出图像P3(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 ;

本发明步骤(2)中权重值Wn通过使用欧式距离的倒数计算得出5×5大小内各像素点对应的位置坐标(x,y)与中心像素点的位置坐标(x0,y0)计算求出,权重模板如图2所示,其计算公式为: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:

Figure BDA0002147558900000061
Figure BDA0002147558900000061

对步骤(2)中所得权重Wn(x,y)进行归一化计算得到归一化后的权重值wnThe normalized calculation is performed on the weight W n (x, y) obtained in step (2) to obtain the normalized weight value w n :

Figure BDA0002147558900000062
Figure BDA0002147558900000062

将所得权重经过处理后得到新的权重值后,步骤(4)通过求得的权重值wn对中心像素点(i,j)周围3×3大小区域内像素点对应的灰度值g(i,j)进行计算,滤波模板如图3所示,得到中心像素点(x0,y0)的灰度值g(i,j)为: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=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)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)

通过处理后新的灰度值得到图像P1The image P 1 is obtained by the new gray value after processing.

步骤5通过二维图像函数f(x,y)的拉普拉斯变换是各向同性的二阶导数:Step 5 The Laplace transform of the two-dimensional image function f(x, y) is the isotropic second derivative:

Figure BDA0002147558900000063
Figure BDA0002147558900000063

在二维图像函数f(x,y)中,x和y两个方向的二阶差分

Figure BDA0002147558900000064
Figure BDA0002147558900000065
为:In the two-dimensional image function f(x, y), the second-order difference of the two directions of x and y
Figure BDA0002147558900000064
and
Figure BDA0002147558900000065
for:

Figure BDA0002147558900000066
Figure BDA0002147558900000066

将x和y两个方向的二阶差分

Figure BDA0002147558900000067
Figure BDA0002147558900000068
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
Figure BDA0002147558900000067
and
Figure BDA0002147558900000068
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:

Figure BDA0002147558900000069
Figure BDA0002147558900000069

使用如图4所示的拉普拉斯算子模板,经过拉普拉斯算子处理后得到图像P2,该图像包含了原图像的细节及边缘信息。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.

步骤(6)流程如图5所示,将经过权重均值滤波的图像P1与经过拉普拉斯算子锐化后的图像P2进行图像分解,得到拉普拉斯层,本发明中采用三层设计,高层的拉普拉斯图像均来自于前一层的降采样。将经过处理得到的P1、P2各层图像进行融合处理后进行图像的扩展与金字塔的重构从而得到最终的融合图像P3,处理完成后的图像P3可以有效的增强图像细节并降低图像的噪声。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.

以上所述仅是本发明的优选实施方式,应当指出的是,对于本技术领域的普通技术人员来说,在不脱离本发明原理的前提下,还可以做出若干改进和润饰,这些改进和润饰也应视为本发明的保护范围。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 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:
Figure FDA0002147558890000011
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):
Figure FDA0002147558890000021
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:
Figure FDA0002147558890000022
wherein
Figure FDA0002147558890000023
And
Figure FDA0002147558890000024
comprises the following steps:
Figure FDA0002147558890000025
the laplace operator is found to be:
Figure FDA0002147558890000026
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.
CN201910689889.1A 2019-07-29 2019-07-29 A method of infrared image detail enhancement and noise suppression Pending CN111652808A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910689889.1A CN111652808A (en) 2019-07-29 2019-07-29 A method of infrared image detail enhancement and noise suppression

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910689889.1A CN111652808A (en) 2019-07-29 2019-07-29 A method of infrared image detail enhancement and noise suppression

Publications (1)

Publication Number Publication Date
CN111652808A true CN111652808A (en) 2020-09-11

Family

ID=72349169

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910689889.1A Pending CN111652808A (en) 2019-07-29 2019-07-29 A method of infrared image detail enhancement and noise suppression

Country Status (1)

Country Link
CN (1) CN111652808A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112258448A (en) * 2020-09-15 2021-01-22 郑州金惠计算机系统工程有限公司 Fine scratch detection method, fine scratch detection device, electronic equipment and computer-readable storage medium
CN114202466A (en) * 2020-09-18 2022-03-18 北京金山云网络技术有限公司 Image noise reduction method, device, electronic device and storage medium

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100142790A1 (en) * 2008-12-04 2010-06-10 New Medical Co., Ltd. Image processing method capable of enhancing contrast and reducing noise of digital image and image processing device using same
CN103208105A (en) * 2013-05-02 2013-07-17 中国电子科技集团公司第二十八研究所 Infrared image detail enhancement and noise adaptive inhibition method
CN107784642A (en) * 2016-08-26 2018-03-09 北京航空航天大学 A kind of infrared video and visible light video method for self-adaption amalgamation
CN109919861A (en) * 2019-01-29 2019-06-21 浙江数链科技有限公司 Infrared image enhancing method, device, computer equipment and storage medium

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100142790A1 (en) * 2008-12-04 2010-06-10 New Medical Co., Ltd. Image processing method capable of enhancing contrast and reducing noise of digital image and image processing device using same
CN103208105A (en) * 2013-05-02 2013-07-17 中国电子科技集团公司第二十八研究所 Infrared image detail enhancement and noise adaptive inhibition method
CN107784642A (en) * 2016-08-26 2018-03-09 北京航空航天大学 A kind of infrared video and visible light video method for self-adaption amalgamation
CN109919861A (en) * 2019-01-29 2019-06-21 浙江数链科技有限公司 Infrared image enhancing method, device, computer equipment and storage medium

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
徐敏;赵毅强;刘文娟;: "基于长线列探测器的图像非均匀性研究与校正", 传感器与微系统 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112258448A (en) * 2020-09-15 2021-01-22 郑州金惠计算机系统工程有限公司 Fine scratch detection method, fine scratch detection device, electronic equipment and computer-readable storage medium
CN114202466A (en) * 2020-09-18 2022-03-18 北京金山云网络技术有限公司 Image noise reduction method, device, electronic device and storage medium

Similar Documents

Publication Publication Date Title
Lau et al. Restoration of atmospheric turbulence-distorted images via RPCA and quasiconformal maps
CN111915530B (en) An end-to-end haze concentration adaptive neural network image dehazing method
Rajkumar et al. A comparative analysis on image quality assessment for real time satellite images
CN103533214B (en) Video real-time denoising method based on kalman filtering and bilateral filtering
CN101639938B (en) Image restoration method based on double-edge wave filter and margin deconvolution
CN108629744B (en) Image enhancement method
CN112184604B (en) Color image enhancement method based on image fusion
CN112507997A (en) Face super-resolution system based on multi-scale convolution and receptive field feature fusion
CN104599242B (en) Use the fuzzy core method of estimation of multiple dimensioned non local canonical
CN111091503A (en) Image defocus blur method based on deep learning
WO2015192316A1 (en) Face hallucination using convolutional neural networks
CN107203985A (en) An End-to-End Deep Learning Framework for Multi-exposure Image Fusion
Liu et al. Pd-gan: Perceptual-details gan for extremely noisy low light image enhancement
Wang et al. A graph-based joint bilateral approach for depth enhancement
CN112927135B (en) A polarization image interpolation method based on edge perception
CN117726537B (en) SAR image denoising network method and system based on adaptive multi-scale feature fusion AMFFD-Net
CN113222825A (en) Infrared image super-resolution reconstruction method based on visible light image training and application
CN111652809A (en) A Noise Suppression Method for Infrared Image with Enhanced Details
He et al. Atmospheric turbulence mitigation based on turbulence extraction
CN110415193A (en) Restoration method of low-illumination fuzzy image in coal mine
CN115689960A (en) A fusion method of infrared and visible light images based on adaptive illumination in nighttime scenes
CN102222327A (en) Image denoising method based on Treelet transformation and minimum mean-square error estimation
Arulkumar et al. Super resolution and demosaicing based self learning adaptive dictionary image denoising framework
CN103955934A (en) Image blurring detecting algorithm combined with image obviousness region segmentation
CN118982467A (en) A polarization image fusion method based on multi-scale transformation and image enhancement

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
AD01 Patent right deemed abandoned
AD01 Patent right deemed abandoned

Effective date of abandoning: 20240105