TW202111601A - Maritime image filter method and processing device - Google Patents

Abstract

A method for filtering a maritime image noise at least includes an image processing device and a photoelectric sensor. The image processing device is implemented by a processing unit. The method includes at least the following steps: (A) performing a dark channel spatial domain estimation on an original input image to obtain a dark channel two-dimensional matrix of the image; (B) performing a photometric spatial domain estimation on the original input image to obtain a photometric two-dimensional matrix of the image; (C) performing a convolution operation on the dark channel two-dimensional matrix and the photometric two-dimensional matrix to calculate an estimated value of a credibility of an object on the water and obtaining a two-dimensional credibility matrix. The two-dimensional credibility matrix can be used in the image to filter interferences and noises associated with water surface phenomenon to achieve the filtering effect of the maritime image.

Description

Water image noise filtering method and image processing device

The present invention relates to an image data processing, in particular to a water image noise filtering method and its processing device for water surface characteristics.

It is known to use Electro-optical Sensors to capture optical data of the surrounding environment, and to record and store it as image or video data, which is widely used in applications such as visual detection or surveillance systems.

Although digital image processing and computer vision technology have been developed for many years, and many image processing technologies and devices have been successfully developed, however, currently there are few technologies for filtering (Filter) of water images or video data, because in the environment where the water surface appears Among them, the special physical properties of water produce diverse phenomena such as Dynamic Background, Waves, Wakes, Foam, and Glints. Therefore, it is difficult to establish conventional image processing techniques. The model is used to reduce the effect of its phenomenon on the image or video data, resulting in unsatisfactory filtering and detection effects of objects in the water image or video data, and further directly reducing the subsequent visual detection or monitoring system such as recognition, tracking, Results of behavior analysis and application.

Although digital image processing and computer vision technologies have been developed for many years, and many image processing technologies and devices have been successfully developed, the accuracy of the maritime filtering effect using the conventional image or vision processing technology still needs to be enhanced.

Therefore, the conventional image noise filtering processing technology has no effect on maritime filtering. The law is satisfactory.

In view of this, the present invention is particularly used for a water image noise filtering method and an image processing device, which is used to filter the image or video data captured by the optical sensor in the water environment, and improve the image or video data caused by The main purpose is to further reduce the false detection results of the vision system.

In order to achieve the above-mentioned purpose, an image noise filtering method with water characteristics of the present invention is implemented by a processing unit, which at least includes the following steps: (A) Perform dark channel spatial domain estimation on the original input image to obtain the image The dark channel two-dimensional matrix; (B) the original input image is estimated in the photometric space domain to obtain the image's two-dimensional photometric matrix; (C) the dark channel two-dimensional matrix and the photometric two-dimensional matrix are convolved (Convolution) Calculate the credibility estimates of the objects on the water, and obtain a two-dimensional matrix of credibility.

To achieve the above-mentioned purpose, a water-based image noise filtering image processing device of the present invention at least includes an image processing device 100 and a photoelectric sensor 200.

100‧‧‧Image pre-processing module

110‧‧‧Water image noise filter processing module

120‧‧‧Image post-processing module

130‧‧‧Binary operation module

140‧‧‧Object detection, identification and tracking module

200‧‧‧Photoelectric Sensor

111~113‧‧‧Steps of noise filtering processing on water images

Fig. 1 is a block diagram illustrating an example of an image processing device used to implement an embodiment of the method for filtering water image noise according to the present invention; Fig. 2 is a flowchart illustrating an embodiment of the present invention; Fig. 3 is The use state diagram of the present invention in the visual monitoring system; Figure 4 is the use state diagram of the present invention in the image; Figure 5 is the use diagram of the present invention in the water surface image and video data

In order to help your examiners understand the technical features, content and advantages of this creation and the effects that can be achieved, this creation is combined with the drawings and described in detail in the form of embodiments as follows. Refer to Figure 1 to illustrate the implementation of this creation. An image processing device 100 and a photoelectric sensor 200 are invented as an embodiment of the marine environment.

Refer to Figure 2 for filtering steps of water image noise: First, a method for filtering water image noise with characteristics of water is implemented by a processing unit, which at least includes the following steps: (A) Perform dark channel spatial domain estimation on the original input image, The dark channel two-dimensional matrix of the image is obtained; (B) the original input image is estimated in the photometric space domain to obtain the photometric two-dimensional matrix of the image; (C) the dark channel two-dimensional matrix and the photometric two-dimensional matrix are convolved (Convolution) operation to calculate the estimated value of the credibility of the object on the water, and obtain a two-dimensional matrix of the credibility.

其中，D為暗通道二維矩陣、

為以像素點位置

為中心、大小為n_d×n_d之鄰近點、σ_d為正值參數、η( )函數計算如下式：

其中，I^C為光電感測器所獲得的原始輸入影像(亦即輸入影像SIMG)、R、G、B分別表示紅、綠、藍三種色彩空間。 Please refer to Figure 2. In step 111, the dark channel spatial domain estimate is calculated according to the following formula:

Among them, D is a two-dimensional matrix of dark channels,

In pixels

Is the center, the adjacent point _{with size n d} ×n _{d , σ d} is a positive parameter, and the η() function is calculated as follows:

Wherein, I ^C is the original input image obtained by the photoelectric sensor (i.e., input image SIMG), R, G, B represent the red, green, blue color space.

其中，R為光度二維矩陣、

為以像素點位置

為中心、大小為n_r×n_r之鄰近點、σ_r為一正值參數、δ( )函數計算如下式：

其中，

為以像素點位置

為中心、大小為n_r×n_r之鄰近點。 Secondly, in step 112, the photometric spatial domain estimate is calculated according to the following formula:

Among them, R is the two-dimensional photometric matrix,

In pixels

Is the center, the adjacent point _{with size n r} ×n _{r , σ r} is a positive parameter, and the δ() function is calculated as follows:

among them,

In pixels

It is the adjacent point with the center and the size of n _r ×n _r.

Next, in step 113, the reliability estimate is calculated according to the following formula:

]

[0,1]，獲得可信度二維矩陣圖O，該可信度二維矩陣圖用以達到水面雜訊去除功效之示意圖如圖5所示。 Among them, K is a normalization constant, 0[

]

[0,1], obtain a credibility two-dimensional matrix map O, and a schematic diagram of the credibility two-dimensional matrix map for achieving the effect of removing water surface noise is shown in FIG. 5.

Further, as shown in Fig. 3, the present invention is applied to the use diagram of the visual monitoring system, and the user sets a threshold parameter k * for the two-dimensional matrix diagram of the reliability calculated by the water image noise filtering method, or Using Otsu's Algorithm to automatically calculate the global best binarization threshold k * by maximizing between-class Variances (Maximizing between-class Variances), k * is as follows:

其中，σ²表示類別間之變異量(Variance)經由門檻值參數與二值化運算可獲得去除雜訊之二值化影像。 Next, according to the parameter k * and the two-dimensional matrix of credibility, the binarization operation is performed as shown in the following formula:

Among them, σ ² represents the variation between categories (Variance) through threshold parameters and binarization operations to obtain a binarized image with noise removed.

Furthermore, as shown in FIG. 4, the application diagram of the present invention applied to the image to filter the interference and noise of the water surface phenomenon, and the schematic diagram of achieving the water surface filtering effect is shown in FIG. 5.

Regarding the calculation formulas (1)~(8) in the embodiment, in addition to the calculation through the circuit, it can also be based on the relationship between the various parameters in the formulas (1)~(8) to establish a look-up table Way to complete.

As described above, an image noise filtering image processing device with water characteristics of the present invention includes at least an image processing device 100 and a photoelectric sensor 200, as shown in Figure 1, Figure 2, Figure 3, and Figure 4. The modules and steps in the figure can be implemented by hardware (for example, circuits) or hardware with software (for example, processors with programs), and the modules in the figure can also be combined into fewer Modules may be divided into more modules.

In summary, the present invention provides water surface phenomenon filtering actions for images and videos, so that the input original image can be more high-quality, and thereby produce clearer water objects in the output image. In addition, the embodiment of the present invention also proposes dark channel spatial domain estimation and photometric spatial domain estimation to dynamically adjust the filter intensity according to the light and shadow conditions in the image. In this way, the filter is filtered in response to changes in the environment in which the image processing device is located. Actions can be more adaptable and provide high-quality output images.

Although the present invention has been disclosed in the above embodiments, it is not intended to limit the present invention. Anyone with ordinary knowledge in the relevant technical field can make some changes and modifications without departing from the spirit and scope of the present invention. The scope of protection of the present invention shall be determined by the scope of the attached patent application.

Other features and effects of the present invention will be clearly presented in the embodiments with reference to the drawings, in which: FIG. 1 is a block diagram, exemplarily showing a method for implementing the present invention Figure 2 is a flowchart illustrating an embodiment of the present invention; Figure 3 is a diagram showing the state of use of the present invention in a maritime video monitoring system; Figure 4 is a diagram showing the state of use of the present invention in an on-water video monitoring system Figure 5 shows the usage diagram of the invention on the water surface image and video data.

In summary, the present invention provides a better and feasible marine image processing device, and an application for a patent for invention is filed in accordance with the law; the technical content and technical features of the present invention are disclosed above, but those familiar with this technology may still be based on this The disclosure of the invention makes various substitutions and modifications that do not depart from the creative spirit of the case. Therefore, the protection scope of this creation should not be limited to those disclosed in the embodiments, but should include various replacements and modifications that do not deviate from the present invention, and are covered by the following patent applications.

【Designated Representative Picture of this Case】: Picture (1).

[A brief description of the symbols of this representative picture]:

100‧‧‧Image pre-processing module

110‧‧‧Water image noise filter processing module

120‧‧‧Image post-processing module

200‧‧‧Photoelectric Sensor

【Designated Representative Picture of this Case】: Picture (2).

[A brief description of the symbols of this representative picture]:

111~113‧‧‧Steps of noise filtering processing on water images

Claims (9)

An image noise filtering method with water characteristics, implemented by a processing unit, including the following steps: (A) Perform dark channel spatial domain estimation on the original input image to obtain a two-dimensional dark channel matrix of the image; (B) The original input image is estimated in the photometric space domain to obtain the two-dimensional photometric matrix of the image; (C) Using the credibility estimation, the two-dimensional matrix of the dark channel and the two-dimensional photometric matrix are calculated, and the two-dimensional matrix of credibility is obtained Figure; and (D) global binarization, calculate the maximum variation between classes on the reliability two-dimensional matrix graph to obtain the output binarized image. The method for filtering image noise with maritime characteristics according to claim 1, wherein, in step (A), the dark channel spatial domain estimation value is a spatial domain filtering operation in the pixels of the input image and its neighboring patches (Patch) Value to obtain a two-dimensional matrix of dark channels. The method for filtering image noise with water characteristics according to claim 1, wherein, in step (B), the luminosity spatial domain estimation value is a spatial domain filtering operation in the pixels of the input image and its neighboring patches (Patch) Value to obtain a two-dimensional matrix of luminosity. The method for filtering image noise with water characteristics according to claim 1, wherein, in step (C), the credibility estimated value is the dark channel two-dimensional matrix and the elements of the luminosity two-dimensional matrix. Calculate the result of the Convolution operation to obtain a two-dimensional matrix of credibility. The method for filtering image noise with maritime characteristics according to claim 1, wherein, in step (D), global binarization calculates the maximum inter-class variation for the two-dimensional matrix graph of reliability, which is regarded as the global maximum To optimize the binarization parameter, further, use the binarization parameter to perform a binarization operation on the reliability two-dimensional matrix graph to obtain the output binarized image. The image noise filtering method for water characteristics according to claim 1, wherein the intensity value of the pixel position of the input image on the water surface is inversely proportional to the two-dimensional matrix value of the dark channel, and one of these values is taken The largest one is used to generate the two-dimensional matrix value of the dark channel. The image noise filtering method for water characteristics according to claim 1, wherein the intensity value of the pixel point on the water surface of the input image is proportional to the value of the luminosity two-dimensional matrix, and the maximum and minimum of these values are taken The difference value is used to generate the two-dimensional matrix value of the luminosity. For example, the water image filtering method described in item 1 of the scope of patent application, wherein the credibility estimation calculates the convolution operation value of the average dynamic range width of each dark channel and the photometric dynamic range width to obtain the position of the pixel in the image. Reliability value, which is proportional to the probability of non-aqueous objects in the water surface image. An image processing device for filtering image noise with water characteristics, at least including: a method for filtering image noise with water characteristics as described in item 1 of the patent application, an image processing device 100, and a photoelectric sensor 200. The modules and steps in can be implemented by hardware (for example, circuits) or hardware with software (for example, processors with programs). The modules and steps can also be combined into fewer modules or divided into More modules

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