将不想要的值替换为恰好为正的最接近的索引值 [英] Replacing indices with an unwanted value with their the nearest index's value that happens to be positive

问题描述

此问题与上一个问题不同(解决方案

以下代码将每个负数替换为最接近的非负数.如果在相同的最小距离处有多个非负数，则使用线性顺序的第一个.

无循环

这将建立一个非常大的矩阵作为中间结果.由于内存限制，它可能不适用于大输入.

matrix = [-255 4 6; -5 -4 5; -400 3 16; -6 -7 -8; 13 -5  14];
[r_use, c_use] = find(matrix>=0); % row and column indices of useful values
z_use = r_use+1j*c_use; % same as complex number
[r_neg, c_neg] = find(matrix<0); % row and column indices of negative values
z_neg = r_neg+1j*c_neg; % same as complex number
[~, ind_min] = min(abs(bsxfun(@minus, z_use, z_neg.')), [], 1); % compute distance
    % between each useful value and each negative value. For each negative value, 
    % give  index of nearest useful value. This index is referred to r_use, c_use
ind_use = sub2ind(size(matrix), r_use(ind_min), c_use(ind_min)); % linear indices 
    % of useful values that will replace the negative values
ind_neg = sub2ind(size(matrix), r_neg, c_neg); % linear indices of negative values
matrix(ind_neg) = matrix(ind_use); % replace

之前:

matrix =
  -255     4     6
    -5    -4     5
  -400     3    16
    -6    -7    -8
    13    -5    14

之后:

matrix =
     4     4     6
     4     4     5
     3     3    16
    13     3    16
    13    13    14

有循环

这通过使用循环一次处理负值来减少内存消耗.

matrix = [-255 4 6; -5 -4 5; -400 3 16; -6 -7 -8; 13 -5  14];
[r_use, c_use] = find(matrix>=0); % row and column indices of useful values
z_use = r_use+1j*c_use; % same as complex number
[r_neg, c_neg] = find(matrix<0); % row and column indices of negative values
z_neg = r_neg+1j*c_neg; % same as complex number
for k = 1:numel(z_neg) % for each negative value
    [~, ind_min_k] = min(abs(z_use-z_neg(k))); % compute distance between
    % each useful value and this negative value. Give index of nearest
    % useful value. This index is referred to r_use, c_use
    ind_use_k = sub2ind(size(matrix), r_use(ind_min_k), c_use(ind_min_k));
    % linear index of useful value that will replace this negative value
    ind_neg_k = sub2ind(size(matrix), r_neg(k), c_neg(k)); % linear index
    % of this negative value
    matrix(ind_neg_k) = matrix(ind_use_k); % replace
end

This question is different from the previous question (How to find indices with a negative value and replace the value with the nearest index's value that happens to be positive?). The previous question was to replace an index's unwanted value with its nearest positive index's value on the same row.

This question is to replace the unwanted value with its nearest positive index's value throughout the entire matrix (not only limited to the same row).

• If there is more than one index that is the nearest to the observed index in the entire matrix, take the value randomly (it doesn't really matter tbh).

• I am dealing with a 1003x1170 single matrix. So it would be best if the solution doesn't come with so much overhead. (optional)

For example, if the negative values are unwanted,

[-255  4  6;
   -5 -4  5;
 -400  3  6;
   -6 -7 -8;
    3 -5  4]

becomes

[4 4 6;
 4 5 5;
 3 3 6;
 3 3 6;
 3 4 4]

(The answer doesn't necessarily have to be like this since it takes the nearest value randomly (right/left/up/down whatever, if it makes things easier by adding some consistency, that is also fine.))

Purpose: I am trying to do this to remove the big size of the noise holes in the observed image matrix without killing the sharpness of the image.

Thank you for your supports!

解决方案

The following code replaces each negative number by the nearest non-negative number. If there are several non-negative numbers at the same minimum distance, the first one in linear order is used.

Without loop

This builds a very large matrix as intermediate result. It may not work for large inputs due to memory limitations.

matrix = [-255 4 6; -5 -4 5; -400 3 16; -6 -7 -8; 13 -5  14];
[r_use, c_use] = find(matrix>=0); % row and column indices of useful values
z_use = r_use+1j*c_use; % same as complex number
[r_neg, c_neg] = find(matrix<0); % row and column indices of negative values
z_neg = r_neg+1j*c_neg; % same as complex number
[~, ind_min] = min(abs(bsxfun(@minus, z_use, z_neg.')), [], 1); % compute distance
    % between each useful value and each negative value. For each negative value, 
    % give  index of nearest useful value. This index is referred to r_use, c_use
ind_use = sub2ind(size(matrix), r_use(ind_min), c_use(ind_min)); % linear indices 
    % of useful values that will replace the negative values
ind_neg = sub2ind(size(matrix), r_neg, c_neg); % linear indices of negative values
matrix(ind_neg) = matrix(ind_use); % replace

Before:

matrix =
  -255     4     6
    -5    -4     5
  -400     3    16
    -6    -7    -8
    13    -5    14

After:

matrix =
     4     4     6
     4     4     5
     3     3    16
    13     3    16
    13    13    14

With loop

This reduces memory consumption by working with the negative values one at a time, using a loop.

matrix = [-255 4 6; -5 -4 5; -400 3 16; -6 -7 -8; 13 -5  14];
[r_use, c_use] = find(matrix>=0); % row and column indices of useful values
z_use = r_use+1j*c_use; % same as complex number
[r_neg, c_neg] = find(matrix<0); % row and column indices of negative values
z_neg = r_neg+1j*c_neg; % same as complex number
for k = 1:numel(z_neg) % for each negative value
    [~, ind_min_k] = min(abs(z_use-z_neg(k))); % compute distance between
    % each useful value and this negative value. Give index of nearest
    % useful value. This index is referred to r_use, c_use
    ind_use_k = sub2ind(size(matrix), r_use(ind_min_k), c_use(ind_min_k));
    % linear index of useful value that will replace this negative value
    ind_neg_k = sub2ind(size(matrix), r_neg(k), c_neg(k)); % linear index
    % of this negative value
    matrix(ind_neg_k) = matrix(ind_use_k); % replace
end

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