Gaussian Noise Implementation #29
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We first extract some information from the image - width, height etc., convert to luma and compute gradients. Then, for each pixel (x,y), we compute rho = xcos(theta) + ysin(theta). After a few more transformations and finding the bins with highest values, we generate the resulting image.
Preliminary Hough transform implementation
It is used to improve contrast in images and works by performing enhancement on small 'contextual' regions or neighborhood of each pixel in the given image.
Adaptive Histogram Equalization
Laplacian filter
Fix the horizontal kernel of Sobel filter.
* Mean filter version 1
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If you look at documentation of makeImageR closely you'll notice that it talks about dimensions |
| gaussianNoise image mean sigma g = accBin | ||
| where | ||
| widthMax, heightMax :: Int | ||
| widthMax = ((rows image) - 1) |
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width /= rows, as in the comment.
| where | ||
| widthMax, heightMax :: Int | ||
| widthMax = ((rows image) - 1) | ||
| heightMax = ((cols image) - 1) |
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height /= cols, as in the comment
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Also, please try to make sure that the code at least compiles before submitting a PR. Just run |
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@lehins Sorry sir but actually, I had a query in a part or two and hence left a couple things deliberately. I had a doubt whether this was the correct way. Secondly, I was not sure what list/array to use for the purpose. As in lines 87-90, I'm unsure how to convert and use the generated values from |
An implementation of adding Gaussian noise to images. A few changes yet to be made before producing sample outputs.