Improving and Optimizing Blurring Function

I wrote a very simple sort of blurring function that takes an image and integer (n) as inputs and outputs an image of the same size that divides up the original image into n x n sections and takes the mean of each section and assigns that color to the output image. (Hopefully that makes sense?) I know my code is by no means great and there's plenty of ways to break it, but I was wondering what might be a better way to accomplish this task rather than actually looping through each section and calculating means? Just hoping to learn some tips on how to optimize a function like this or if there's a better way entirely to accomplish this. Thanks!

Here's my code:

inputArg1 is the image and inputArg2 is the integer n

function [outImg] = justChannel(inputArg1,inputArg2)
outImg = zeros(size(inputArg1));
[x,y] = size(inputArg1);
deltaX = x/inputArg2;
deltaY = y/inputArg2;
redChannel = inputArg1(:,:,1);
greenChannel = inputArg1(:,:,2);
blueChannel = inputArg1(:,:,3);
across=0;
down=0;
while(across < inputArg2)
    while(down < inputArg2)
        currentX = floor(across * deltaX + 1);
        currentY = floor(down * deltaY + 1);
        nextX = ceil(currentX + deltaX - 1);
        nextY = ceil(currentY + deltaY - 1);
        redMean = mean(mean(redChannel(currentX:nextX,currentY:nextY)));
        outImg(currentX:nextX,currentY:nextY,1) = redMean;
        greenMean = mean(mean(greenChannel(currentX:nextX,currentY:nextY)));
        outImg(currentX:nextX,currentY:nextY,2) = greenMean;
        blueMean = mean(mean(blueChannel(currentX:nextX,currentY:nextY)));
        outImg(currentX:nextX,currentY:nextY,3) = blueMean;
        down=down+1;
    end
    across = across + 1;
    down=0;
end
outImg = uint8(outImg);
imshow(outImg);
end

Answer 1

[Matt J]

Using sepblockfun from the File Exchange,

https://www.mathworks.com/matlabcentral/fileexchange/48089-separable-block-wise-operations

n=inputArg2;

outImg = repelem(  sepblockfun(inputArg1,[n,n,1],'mean')   , n,n,1);