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This doesn't use wavelets afaics (they are not mentioned in the article), it uses something called 'Laplacian Pyramid'. That can be used separate a signal by fr
by danwills 5y ago
This doesn't use wavelets afaics (they are not mentioned in the article), it uses something called 'Laplacian Pyramid'. That can be used separate a signal by frequency but seems to do the whole image at once. Looks to be the good old add-laplacian (ie 'blur') and subtract-from-the-original method (to get remaining high frequency).
I think wavelets usually have a limited extent in space (depending which basis shape you choose I guess).
Maybe they could make use of FFT for this separation, but it's probably hard to beat blur-and subtract for simplicity of implementation.
- SimplyUnknown 5y agoEssentialy, you do the same thing as an FFT for separation. A gaussian blur is a low-pass filter, and functionally the same as selecting all frequency component within a radius r of the DC component (assuming the DC component is in the center of the image). The radius r depends on the width of the gaussian kernel. The only difference could be in computational time (might be that the FFT is faster) and will be different if you use a different kind of blurring (i.e. non-gaussian such as selecting a square region in the frequency domain).
- danwills 5y agoAgreed! And in cases where the convolution kernel (bokeh, but in frequency space) is something more complicated than gaussian, FFT can definitely be a very good option (kernel/bokeh-res can be near-ish to the res of the image-to-convolve, for example, and full-colour kernels/bokehs too).
- dimatura 5y agoI don't have any quantitative evidence on computational performance of the FFT versus the typical laplacian pyramid technique, but I'd be surprised if FFT is faster. The classical laplacian pyramid -- developed in the 80s -- is really quite simple and fast. http://www.liralab.it/teaching/SINA_10/papers/burt-adelson1983.pdf http://www.liralab.it/teaching/SINA_10/papers/burt-adelson19...