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Formulation of the Problem I am solving the problem under the following assumptions: The blurring operator is Linear and Spatially Invariant (Hence applied by convolution). The blurring operator is known. There is a measurement noise. So the model is: $$ \boldsymbol{y} = H \boldsymbol{x} + \boldsymbol{n} $$ Where $ H $ is the matrix form of the blurring ...


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In general, the approach to take, is to have a local feature which has high value for such areas in the image. There are many approaches to shape such a feature. Probably the easiest one would be by local variance. I tried 3 different approaches to this: Local Variance by a Filter. Local Variance of a Super Pixel. Using the Weak Texture from Noise Level ...


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2D wavelet transform is well suited. It's an extension of 1D CWT where we correlate wavelets of different center frequencies and "scales" (widths in time domain). Wavelets can be calibrated to detect fast or slow variations over small, localized or large, spread out parts of image The output is a 3D array indexed as: x: x-coordinate of wavelet ...


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If your question is not using as yet unconventional terminology of image classification with deep learning tools (and 'YUK' is a typo, you mean YUV), it hardly ever makes sense to talk about color spaces of 1-bit-per-color and even 3-bit-depth images, so we assume that you are looking for an algorithm and data structures used when doing color space ...


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The operator $ \mathbb{E} \left[ \cdot \right] $ is the Expectation Operator. In the context above it means you run over all the pairs of x, y and average the values.


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There are 2 things to remember in the context you're asking: Image as Column Stacked Vector It seems what's confuses you is that you think the operator is working on the image in its 2D form (Matrix). Yet in practice you should reshape it into a column vector. In my answer you linked to, have a look how it operates on the image in mIx = reshape(mDh * mI(:), ...


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First, let's analyze the problem by formulating it. The model is given by: $$ \boldsymbol{y} = H \boldsymbol{x} + \boldsymbol{n} $$ Where $ \boldsymbol{y} $ is the given image, $ H $ is an unknown linear shift invariant blur operator, $ \boldsymbol{x} $ is the image we're after and $ \boldsymbol{n} $ is the added noise. We'll assume it is a White Noise (...


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Remark: This is adapted from How to Solve Image Deblurring with Total Variation Prior Using ADMM? Formulation of the Problem I am solving the problem under the following assumptions: The blurring operator is Linear and Spatially Invariant (Hence applied by convolution). The blurring operator is known. There is a measurement noise. So the model is: $$ \...


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the algorithm I worked on is better. Whenever you use the word "better" you need to define by what metric: could be all sort of things "runs faster", "less cpu or memory", "more robust against noise", etc. And yes, these do require a quantifiable definition, otherwise you can't rank order them. In your case you want ...


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Probably not what you want to hear, but I'd suggest showing the different images to a large number of people (more than 20) and getting them to choose which binary image best represents the color image. Collate the votes. See if other people think your images are better than the alternative. What you're trying to do is to show that your technique better ...


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There are multiple answers on site which deals with your problem. The actual solution depends on your choice for how to deal with the borders. Assume Zero Value Borders See Replicate MATLAB's `conv2()` in Frequency Domain Assume Circular Borders Applying Image Filtering (Circular Convolution) in Frequency Domain Assume Replicate Borders Applying 2D ...


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In the case above the mathematics are pretty simple: Use cascaded Haar filters to extract features from the image. You may have a look at Haar Cascade Classifiers in OpenCV Explained Visually. Use Ada Boost to generate an Ensemble of classifiers to detect a face. For overview of this approach (First done by the Viola Jones Detector) you may have a look at ...


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Near IR images are typically monochrome images from cameras where the IR filter has been removed, causing the inherent sensitivity of IR of camera sensors to dominate, and/or an extra filter that attenuates visible light. Typical characteristics of IR images is that foliage become very bright, sky and water is dark, eyes become «creepy», skin artifacts are ...


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