Avoiding Boundaries `if` Cases When Filtering Image Matrix

I have the following MATLAB Code which I want to optimize.

It's related to an image matrix (2D) which I want to filter.

Though it is MATLAB code, the same problem would arise in C code.

The problem is that I'm trying to access neighbors of the pixel which might cause problems at the boundaries of the image matrix.

vWeightsCoeff = zeros((2 * numRows * numCols), 1);

weightsIdx = -3;

% Filteration
for iColIdx = 1:numCols
    for iRowIdx = 1:numRows
        if(mod((iRowIdx + iColIdx), 2) == 0)
            continue;
        end

        weightsIdx = weightsIdx + 4;

        refPixel = mOutputImage(iRowIdx, iColIdx);

        if(iRowIdx  1)
            relPixel2 = mOutputImage((iRowIdx - 1), iColIdx);
            relWeight2 = vRangeTable(round((refPixel - relPixel2) * RANGE_RESOLUTION) + RANGE_CENTER_IDX);
        else
            relPixel2 = 0;
            relWeight2 = 0;
        end

        if(iColIdx  1)
            relPixel4 = mOutputImage(iRowIdx, (iColIdx - 1));
            relWeight4 = vRangeTable(round((refPixel - relPixel4) * RANGE_RESOLUTION) + RANGE_CENTER_IDX);
        else
            relPixel4 = 0;
            relWeight4 = 0;
        end

        weightsNormalizationFactor = PREDICT_NORMALIZATION_FACTOR / (relWeight1 + relWeight2 + relWeight3 + relWeight4);

        relWeight1 = relWeight1 * weightsNormalizationFactor;
        relWeight2 = relWeight2 * weightsNormalizationFactor;
        relWeight3 = relWeight3 * weightsNormalizationFactor;
        relWeight4 = relWeight4 * weightsNormalizationFactor;

        mOutputImage(iRowIdx, iColIdx) = mOutputImage(iRowIdx, iColIdx) - ((relWeight1 * relPixel1) + (relWeight2 * relPixel2) + (relWeight3 * relPixel3)+ (relWeight4 * relPixel4));

        vWeightsCoeff(weightsIdx:(weightsIdx + 3)) = [relWeight1; relWeight2; relWeight3; relWeight4];

    end
end

Just to clarify the variables:

• mOutputImage is the image matrix.

• numCols and numRows are the matrix dimensions.

• vWeightsCoeff is t

visiting black chess board squares

You might do a mod2 pre-calculation of mod(iRowIdx + iColIdx, 2) by creating a chess board boolean matrix before the loop, using matrix operations.

But much better would be to step iColIdx by 2. And have iRowIdx start at 1 or 2 as appropriate, and then step it by 2.

boundary checking

Your matrix now is C x R. Simply allocate a (C+2) x (R+2) matrix, so you have zeros running along all four sides. Then iterate over just the middle, rather than over the whole matrix as you are now. So iColIdx for example would start at 2 and stop one short of the end.

Doing this will delete a great many ifs, to give you Version2 of the code. You might even find that puts you in a position to produce Version3, which takes advantage of matrix operations instead of scalar operations, pushing much of the looping work down into matlab primitives, which would be faster.

style

It would have been helpful to see the method signature, but your English description that mOutputImage is the only return value worked well enough.

Typo: % Filteration

It would be natural to express the normalization e.g. like this:

relWeight4 *= weightsNormalizationFactor;

Leave a blank after if, please.

The extra parentheses in mod and places like this:

relPixel1 = mOutputImage((iRowIdx + 1), iColIdx);

are a little odd, you could safely elide them.

StackExchange Code Review Q#54262, answer score: 2