add ROF ADMM solver

[johnnychen94]

This is currently incomplete, the benchmark shows that it's already faster than the MATLAB equivalent version. Yet I think we still have more room for optimization wrt the FFT-related operations, so I opened this draft to see if there are any suggestions on this.

using ImageFiltering, ImageCore, TestImages, ImageShow
using ImageFiltering.Models

img = Float32.(testimage("cameraman"))
noisy_img = img .+ 0.25 .* randn(size(img))

@btime solve_ROF_ADMM($noisy_img, 0.25, 0.02; num_iters=100);
# julia + FFTW v1.1.1: 856.001 ms (104403 allocations: 639.15 MiB)
# julia + FFTW v1.4.5: 1.275 s (8316 allocations: 632.49 MiB)
# matlab: 1.851779s

FFTW has some performance regression here: JuliaMath/FFTW.jl#224

[johnnychen94]

This is the performance hot spot:

I guess we can use in-place version fft! or real version rfft to boost it.

[codecov]

Codecov Report

❌ Patch coverage is 0% with 50 lines in your changes missing coverage. Please review.
✅ Project coverage is 89.42%. Comparing base (424523c) to head (68cbe6d).
⚠️ Report is 36 commits behind head on master.

Files with missing lines	Patch %	Lines
src/models.jl	0.00%	50 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master     #238      +/-   ##
==========================================
- Coverage   91.86%   89.42%   -2.45%     
==========================================
  Files          12       12              
  Lines        1647     1692      +45     
==========================================
  Hits         1513     1513              
- Misses        134      179      +45     

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