optimized sobel in matlab

To optimize Sobel edge detection algorithm in MATLAB, we can follow the below techniques:

1. Pre-allocate the memory for the output image: Instead of letting MATLAB allocate the memory dynamically, we can create a matrix with zeroes and pass it to the Sobel function.

main.m
outputImage = zeros(size(inputImage));
sobelImage = edge(inputImage, 'Sobel');
outputImage(:, :, 1) = sobelImage;
outputImage(:, :, 2) = sobelImage;
outputImage(:, :, 3) = sobelImage;
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2. Use a GPU: If we have access to a GPU, we can use it to speed up the Sobel function. We can use the gpuArray function to convert the input image to a GPU array and then use the gpuArray function again to bring the output back to the CPU.

main.m
inputImageGPU = gpuArray(inputImage);
outputImageGPU = edge(inputImageGPU, 'Sobel');
outputImage = gather(outputImageGPU);
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3. Resize the input image: If the input image is very large, we can resize it to a smaller size before applying the Sobel filter. This can significantly reduce the processing time.

main.m
inputImageResized = imresize(inputImage, 0.5); % Resize image by 50%
outputImage = edge(inputImageResized, 'Sobel');
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4. Use multithreading: MATLAB has a built-in multithreading feature that can speed up the Sobel function by distributing its operations over multiple CPU cores. To enable this feature, we can set the NumThreads property of the edge function.

main.m
originalNumThreads = maxNumCompThreads('automatic');
maxNumCompThreads(4); % Set to 4 threads
outputImage = edge(inputImage, 'Sobel');
maxNumCompThreads(originalNumThreads); % Reset to original value
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By using these techniques, we can optimize the Sobel edge detection algorithm in MATLAB and achieve faster processing times for image processing applications.