METHOD FOR REDUCING NOISE DURING AES DETECTION

Abstract

The present disclosure provides a method for reducing noise in AES detection, including steps: obtaining G based on a sub-array {tilde over (Z)} of detection data; for each element in G, forming a set of data using three adjacent elements including the element in a column direction, and sorting the set of data in a descending order to obtain an array {tilde over (D)}; performing normalization processing on the array {tilde over (D)} to obtain an array D; for each element in {tilde over (Z)}, forming a set of data using three adjacent elements including the element in the column direction, and sorting the set of data in a descending order to obtain an array U of m rows by n columns; calculating a noise difference value in the column direction, i.e., an array C of m rows by n−1 columns; formulating a noise array N of m rows by n columns; and constructing a new sub-array P.sup.N.

Claims

1. A method for reducing noise in AES detection, comprising steps of: obtaining G based on a sub-array {tilde over (Z)} of detection data:
G=(S.sub.x{tilde over (Z)}).sup.2+(S.sub.y{tilde over (Z)}).sup.2+(S.sub.r{tilde over (Z)}).sup.2+(S.sub.l{tilde over (Z)}).sup.2, where and s.sub.x, S.sub.y, S.sub.r, and S.sub.l represents a sobel horizontal gradient operator, a sobel vertical gradient operator, a sobel right diagonal gradient operator, and a left diagonal gradient operator, respectively, and S.sub.xZ.sup.˜, S.sub.yZ.sup.˜, S.sub.rZ.sup.˜, and S.sub.rZ.sup.˜ each represent convolution processing of a sobel operator on Z.sup.˜ with a step of 1, a convolution edge being processed by filling 0 or by symmetrization, an influence of the edge on a result being negligible, and where $S_x=\begin{array}{ccc}-1& -2& -1\\ 0& 0& 0\\ 1& 2& 1\end{array},S_y=\begin{array}{ccc}-1& 0& 1\\ -2& 0& 2\\ -1& 0& 1\end{array},S_r=\begin{array}{ccc}0& 1& 2\\ -1& 0& 1\\ -2& -1& 0\end{array},S_I=\begin{array}{ccc}-2& -1& 0\\ -1& 0& 1\\ 0& 1& 2\end{array},$ and a size of {tilde over (Z)} is (m, n); adding one column of zero values to both a left end and a right end of G for only a calculation purpose in this step without changing an original size of G, and recording the added columns as a 0-th column and a (n+1)-th column; and for each element in G, forming a set of data using three elements comprising the element and a previous element and a next element of the element in a column direction, and sorting the set of data in a descending order: G.sub.i,d≥G.sub.i,e≥G.sub.i,f, to obtain an array {tilde over (D)}: ${\tilde{D}}_{i,j}=\frac{\alpha }{G_{i,d}}+\frac{\beta }{G_{i,e}}+\frac{\gamma }{G_{i,f}},$ where α=0.618, β=(1−α)*α, and γ=(1−α).sup.2; and d, e, and f are different from each other and are each taken from a set {j, (j−1), (j+1)}, where 1≤i≤m and 1≤j≤n; performing normalization processing on the array {tilde over (D)} to obtain an array D; adding one column of zero values to both a left end and a right end of {tilde over (Z)} for only a calculation purpose in this step without changing an original size of {tilde over (Z)}, and recording the added columns as a 0-th column and a (n+1)-th column; and for each element in {tilde over (Z)}, forming a set of data using three elements comprising the element in {tilde over (Z)} and a previous element and a next element of the element in {tilde over (Z)} in the column direction, and sorting the set of data in a descending order: {tilde over (Z)}.sub.i,g≥{tilde over (Z)}.sub.i,h≥{tilde over (Z)}.sub.i,k, to obtain an array U of m rows by n columns: $U_{i,j}=\sqrt{\alpha *{\left({\stackrel{\sim }{Z}}_{\mathrm{mean}}-{\tilde{Z}}_{i,k}\right)}^2+\beta *{\left({\tilde{Z}}_{\mathrm{mean}}-{\tilde{Z}}_{i,h}\right)}^2+\gamma *{\left({\tilde{Z}}_{\mathrm{mean}}-{\stackrel{\sim }{Z}}_{i,g}\right)}^2},$ where g, h, and k are different from each other and are each taken from a set {j, (j−1), (j+1)}; and ${\tilde{Z}}_{\mathrm{mean}}=\frac{1}{3}\left({\tilde{Z}}_{i,j}+{\tilde{Z}}_{i,j-1}+{\tilde{Z}}_{i,j+1}\right),$ where 1≤i≤m and 1≤i≤n; calculating a noise difference value in the column direction, the noise difference value being an array C of m rows by n−1 columns: $C_{i,j}=\frac{{\tilde{Z}}_{i,j}}{{\tilde{Z}}_{j\sim \max }}*{.Math.}_{i=1}^{m}0.5*\left[D_{i,j}*\left({\tilde{Z}}_{i,j+1}-{\tilde{Z}}_{i,j}\right)+U_{i,j}\right],$ where {tilde over (Z)}.sub.j˜max is a maximum value of a column in which the element is located, 1≤i≤m, and 1≤j≤n; formulating a noise array N of m rows by n columns as: $\{\begin{array}{c}{N}_{i,x}=01\le i\le m\\ N_{i,j+1}=N_{i,j}+C_{i,j}1\le i\le m,1\le j<n,j\ge x+1\\ N_{i,j-1}=N_{i,j}-C_{i,j-1}1\le i\le m,1<j<n,j<x\end{array},$ where x is a column coordinate of a maximum value in the array {tilde over (Z)}; and constructing a new sub-array P.sup.N:
P.sub.i,j.sup.N={tilde over (Z)}.sub.i,j−N.sub.i,j, where 1≤i≤m and 1≤i≤n.

2. The method for reducing noise in AES detection according to claim 1, wherein the normalization processing is performed by: dividing each element by a sum of values in a column where the element is located, to obtain the array D.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The disclosed content of the present disclosure will become more understandable by referring to the accompanying drawings. It will be readily understood by those skilled in the art that these drawings are merely used for illustrating the technical solutions of the present disclosure, and are not intended to limit the scope of the present disclosure. In the drawings:

[0023] FIG. 1 is a flowchart of a method for reducing noise in AES detection according to an embodiment of the present disclosure;

[0024] FIG. 2 is a standard curve of Ba233.525 according to an embodiment of the present disclosure;

[0025] FIG. 3 is a standard curve of Cu324.754 according to an embodiment of the present disclosure;

[0026] FIG. 4 is a standard curve of Cr283.563 according to an embodiment of the present disclosure; and

[0027] FIG. 5 is a standard curve of Mn257.610 according to an embodiment of the present disclosure.

DETAILED DESCRIPTION

[0028] FIGS. 1 to 5 and the following description describe optional embodiments of the present disclosure to teach those skilled in the art about how to implement and reproduce the present disclosure. In order to teach the technical solutions of the present disclosure, some conventional aspects have been simplified or omitted. It should be understood by those skilled in the art that variations or replacements originating from these embodiments will be within the scope of the present disclosure. Those skilled in the art should understand that the following features can be combined in various ways to form a plurality of variants of the present disclosure. Thus, the present disclosure is not limited to the following optional embodiments, but is only defined by the claims and their equivalents.

Embodiment 1

[0029] FIG. 1 schematically shows a flowchart of a method for reducing noise in AES detection according to an embodiment of the present disclosure. As illustrated in FIG. 1, the method for reducing noise in AES detection includes the following steps A1 to A7:

[0030] At step A1, a multi-gradient quadratic sum G is obtained based on a sub-array {tilde over (Z)} of detection data. A specific manner is as follows: since an image of a real object generated by an optical system is a Gaussian distribution two-dimensional light spot, and data read by a detector is also a two-dimensional array, introducing Sobel operators including a vertical operator, a horizontal operator, and diagonal operators in two directions, which are recorded as S.sub.x, S.sub.y, S.sub.r, and S.sub.l; and

G=(S.sub.x{tilde over (Z)}).sup.2+(S.sub.y{tilde over (Z)}).sup.2+(S.sub.r{tilde over (Z)}).sup.2+(S.sub.l{tilde over (Z)}).sup.2,

where

[00007]$S_x=\begin{array}{ccc}-1& -2& -1\\ 0& 0& 0\\ 1& 2& 1\end{array},S_y=\begin{array}{ccc}-1& 0& 1\\ -2& 0& 2\\ -1& 0& 1\end{array},S_r=\begin{array}{ccc}0& 1& 2\\ -1& 0& 1\\ -2& -1& 0\end{array},S_I=\begin{array}{ccc}-2& -1& 0\\ -1& 0& 1\\ 0& 1& 2\end{array},$

and a size of {tilde over (Z)} is (m, n).

[0031] At step A2, one column of zero values is added to both a left end and a right end of G for only a calculation purpose in this step without changing an original size of G, and the added columns are recorded as a 0-th column and a (n+1)-th column; and for each element in G, a set of data is formed by using three elements comprising the element and a previous element and a next element of the element in a column direction, and the set of data is sorted in a descending order: G.sub.i,d≥G.sub.i,e≥G.sub.i,f, to obtain an array {tilde over (D)}:

[00008]${\tilde{D}}_{i,j}=\frac{\alpha }{G_{i,d}}+\frac{\beta }{G_{i,e}}+\frac{\gamma }{G_{i,f}},$

[0032] where α=0.618, β=(1−α)*α, and γ=(1−α).sup.2; and d, e, and f are different from each other and are each taken from a set {j, (j−1), (j+1)}, where 1≤i≤m and 1≤j≤n.

[0033] At step A3, normalization processing is performed on the array {tilde over (D)}, i.e., each element in the array is divided by a sum of values in a column where the element is located to obtain an array D.

[0034] At step A4, one column of zero values is added to both a left end and a right end of {tilde over (Z)} for only a calculation purpose in this step without changing an original size of {tilde over (Z)}, and the added columns are recorded as a 0-th column and a (n+1)-th column; and for each element in {tilde over (Z)}, a set of data is formed by using three elements including the element in {tilde over (Z)} and a previous element and a next element of the element in {tilde over (Z)} in the column direction, and the set of data is sorted in a descending order: {tilde over (Z)}.sub.i,g≥{tilde over (Z)}.sub.i,h≥{tilde over (Z)}.sub.i,k, to obtain an array U of in rows by n columns:

[00009]$U_{i,j}=\sqrt{\alpha *{\left({\stackrel{\sim }{Z}}_{\mathrm{mean}}-{\tilde{Z}}_{i,k}\right)}^2+\beta *{\left({\tilde{Z}}_{\mathrm{mean}}-{\tilde{Z}}_{i,h}\right)}^2+\gamma *{\left({\tilde{Z}}_{\mathrm{mean}}-{\stackrel{\sim }{Z}}_{i,g}\right)}^2},$

where g, h, and k are different from each other and are each taken from a set {j, (j−1), (j+1)}; and

[00010]${\tilde{Z}}_{\mathrm{mean}}=\frac{1}{3}\left({\tilde{Z}}_{i,j}+{\tilde{Z}}_{i,j-1}+{\tilde{Z}}_{i,j+1}\right),$

where 1≤i≤m and 1≤j≤n.

[0035] At step A5, a noise difference value in the column direction is calculated, and the noise difference value is an array C of m rows by n−1 columns:

[00011]$C_{i,j}=\frac{{\tilde{Z}}_{i,j}}{{\tilde{Z}}_{j\sim \max }}*{.Math.}_{i=1}^{m}0.5*\left[D_{i,j}*\left({\tilde{Z}}_{i,j+1}-{\tilde{Z}}_{i,j}\right)+U_{i,j}\right],$

where {tilde over (Z)}.sub.j˜max is a maximum value of a column in which the element is located, 1≤i≤m, and 1≤j≤n.

[0036] At step A6, a noise array N of m rows by n columns is formulated as:

[00012]$\{\begin{array}{c}{N}_{i,x}=01\le i\le m\\ N_{i,j+1}=N_{i,j}+C_{i,j}1\le i\le m,1\le j<n,j\ge x+1\\ N_{i,j-1}=N_{i,j}-C_{i,j-1}1\le i\le m,1<j<n,j<x\end{array},$

where x is a column coordinate of a maximum value in the array {tilde over (Z)}.

[0037] At step A7, a new sub-array P.sup.N is constructed:

P.sub.i,j.sup.N={tilde over (Z)}.sub.i,j−N.sub.i,j,

where 1≤i≤m and 1≤j≤n.

Example 2

[0038] The method for reducing noise in AES detection according to Example 1 of the present disclosure was applied in ICP-AES.

[0039] In this application example, Ba233.525, Cu324.754, Cr283.563, and Mn257.610 were used for testing:

[0040] 1. A standard curve for Ba233.525, a standard curve for Cu324.754, a standard curve for Cr283.563, and a standard curve for Mn257.610 were constructed and are as illustrated in FIG. 2 to FIG. 5, respectively;

[0041] 2. The instrument detection limit, i.e., 3 times a standard deviation, was detected by loading a blank water sample without adding an algorithm, and the data is as follows (two groups in the middle are taken as controls).

[0042] Test Group 1

TABLE-US-00001 Element Ba4554(45) Cr2835(72) Cu3247(63) Mn2576(79) ppm ppm ppm ppm Mean 0.000292 0.004268 0.005231 0.000033 Standard 0.000020 0.000128 0.000098 0.000005 deviation RSD(%) 6.701030 2.999182 1.881301 16.241564 =1 0.000297 0.004136 0.005307 0.000038 =2 0.000305 0.004288 0.005204 0.000026 =3 0.000287 0.004400 0.005059 0.000033 =4 0.000280 0.004422 0.005190 0.000031 =5 0.000327 0.004169 0.005188 0.000031 =6 0.000301 0.004065 0.005213 0.000028 =7 0.000277 0.004321 0.005378 0.000032 =8 0.000265 0.004333 0.005310 0.000042

[0043] Test Group 2

TABLE-US-00002 Element Ba4554(45) Cr2835(72) Cu3247(63) Mn2576(79) ppm ppm ppm ppm Mean 0.000108 0.000964 0.002402 0.000180 Standard 0.000013 0.000104 0.000067 0.000012 deviation RSD(%) 11.678056 10.776474 2.784478 6.503151 =1 0.000111 0.000836 0.002389 0.000163 =2 0.000106 0.000910 0.002493 0.000188 =3 0.000089 0.001033 0.002470 0.000203 =4 0.000096 0.000935 0.002362 0.000171 =5 0.000104 0.000845 0.002363 0.000175 =6 0.000114 0.001142 0.002343 0.000178 =7 0.000130 0.001031 0.002323 0.000183 =8 0.000117 0.000978 0.002475 0.000180

[0044] 1. Example 1 was used to process original sub-array data corresponding to the above test groups, and the results are shown below.

[0045] Test Group 1 after Algorithm Processing

TABLE-US-00003 Element Ba4554(45) Cr2835(72) Cu3247(63) Mn2576(79) ppm ppm ppm ppm Mean 0.000111 0.002244 0.002972 0.000024 Standard 0.000018 0.000102 0.000075 0.000004 deviation RSD(%) 16.702250 4.565163 2.536015 18.294774 =1 0.000111 0.002172 0.002994 0.000031 =2 0.000112 0.002237 0.002962 0.000026 =3 0.000107 0.002351 0002923 0.000025 =4 0.000107 0.002385 0.002961 0.000023 =5 0.000146 0.002157 0.002916 0.000019 =6 0.000117 0.002083 0.002865 0.000019 =7 0.000103 0.002271 0.003084 0.000020 =8 0.000079 0.002294 0.003071 0.000027

[0046] Test Group 2 after Algorithm Processing

TABLE-US-00004 Element Ba4554(45) Cr2835(72) Cu3247(63) Mn2576(79) ppm ppm ppm ppm Mean −0.000105 0.000266 0.002736 0.000180 Standard 0.000011 0.000092 0.000050 0.000009 deviation RSD(%) −10.856405 34.653129 1.833579 5.139528 =1 −0.000118 0.000116 0.002717 0.000168 =2 −0.000097 0.000251 0.002669 0.000186 =3 −0.000122 0.000359 0.002662 0.000197 =4 −0.000102 0.000252 0.002762 0.000173 =5 −0.000098 0.000166 0.002739 0.000175 =6 −0.000114 0.000391 0.002755 0.000180 =7 −0.000094 0.000315 0.002792 0.000187 =8 −0.000095 0.000283 0.002791 0.000177