<p>I am working with thermal infrared images which subject to quantity of noises.</p>

<p>I found that low rank based approaches such as approaches based on Singular Value Decomposition (SVD) or Weighted Nuclear Norm Metric (WNNM) give very efficient result in terms of reducing the noise while preserving the structure of the information.
Their main drawback is the fact they are quite slow to compute (several minutes per image)
Here is some litterature:</p>

<p><a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7067415" rel="nofollow noreferrer">https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7067415</a></p>

<p><a href="https://arxiv.org/abs/1705.09912" rel="nofollow noreferrer">https://arxiv.org/abs/1705.09912</a></p>

<p>The second paper has some MatLab code available, there is quite a lot of files but the translation to python is should not that complex.</p>

<p>OpenCV implement as well (and it is available in python) a very efficient algorithm on the Non-Local Means algorithm:
<a href="https://docs.opencv.org/master/d5/d69/tutorial_py_non_local_means.html" rel="nofollow noreferrer">https://docs.opencv.org/master/d5/d69/tutorial_py_non_local_means.html</a></p>
Counting number of bright spots in image (python)
<p>I'm trying to develop a way to count the number of bright spots in an <a href="https://i.stack.imgur.com/cmrue.png" rel="nofollow noreferrer">image</a>. The spots should be gaussian point sources, but there is a lot of noise. There are probably on the order of 10-20 actual point sources in this image. My first though was to use a <a href="https://i.stack.imgur.com/2mYDv.png" rel="nofollow noreferrer">gaussian convolution</a> with sigma = 15, which seems to do a good job.</p>

<p>First, is there a better way to isolate these bright spots?</p>

<p>Second, how can I 'detect' the bright spots, i.e. count them? I haven't had any luck with circular hough transforms from opencv.</p>

<p><strong>Edit</strong>: <a href="https://i.stack.imgur.com/zv4MY.png" rel="nofollow noreferrer">Here is the original without gridlines</a>, <a href="https://i.stack.imgur.com/6hefl.png" rel="nofollow noreferrer">here is the convolved image without gridlines</a>.</p>