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 ...
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 ...
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 ...
To have the desired effect of image denoising, the Wiener filter for can only be implemented with a point-spread function (blurring filter) that filters out the noise by averaging in some neighborhood of image pixels. As you do not disclose your program text, one cannot say for sure, but most probably your disappointing result comes from failure to apply the ...
The correct context of the refinement key word is segmentation.
Label Refinement in the context of image segmentation is a step to increase the resolution and understanding of the segmentation.
It can be done by exterior knowledge (Like labels on features) or other optimization steps to have a better results of the segmentation (Which basically labeling of ...