I've modestly reviewed the paper.
I'm skeptical of its speedups and implementation accuracy. It includes time of sampling the wavelets in benchmarks, which is valid, but arguably the main use case is if wavelets are pre-computed and reused. Paper also make several dubious statements that suggest the authors don't really know what they're doing (especially regarding "resolution"), or would even know if they were wrong.
To test its correctness, one should pass in a unit impulse and compare the complex-valued output against known correct implementations:
x = np.zeros(N)
x[N//2] = 1
out0 = cwt0(x)
out1 = cwt1(x)
I believe MATLAB is correct, but in Python I only know of one that's correct and has complex-valued outputs: ssqueezepy, which I authored. SciPy and PyWavelets are not correct.
Moreover, authors conveniently excluded ssqueezepy from their comparisons: they claim x34 speedup against PyWavelets, while ssqueezepy shows x10; this makes them only x3.4 faster than ssqueezepy (but to be fair, they aren't the same configurations).
I'm working on a CWT that should be, worst case, x2 faster than it currently is, and several times faster best case - but one doesn't necessarily need to wait; discussed here.
Is this fCWT a novelty in wavelet concept
There's only one CWT. The only thing that can change is the wavelets or padding used, which isn't the subject of the paper, but the paper misleadingly suggests otherwise with "higher resolution" claims.