I'm trying to understand the different aspects of a wavelet transform. Wavelet power has made enough sense to me as an analogy of the covariance. However, the wavelet coherence does not make sense to me. The notes in the R package 'WaveletComp' states that coherence is analogous to the coefficient of correlation. In my intuition, that would mean coherence between independent time series should be reasonably low. However, this is not the case, as is shown by the R code below.

library(tidyverse)
library(WaveletComp)

complete_noise <- tibble(x = seq(1:1024)) %>% 
      mutate(
        sample_1 = rnorm(x),
        sample_2 = rnorm(x)
  )

wc_noise <- analyze.coherency(complete_noise, c("sample_1", 
    "sample_2"), loess.span = 0, make.pval = F)

wc.image(wc_noise, which.image = "wc", color.key = "interval")

The plot shows the majority of the area as red, which is up near a coherence of 1.0. Why do independent time series have a high coherence in most of these points on the period-time plot?

Edit: I turned off smoothing (it is on by default with WaveletComp) and now the plot shows a coherence of 1 everywhere. I'm taking this to mean the drops in coherence are due to smoothing, which also doesn't make sense to me.