from __future__ import division
import nt_toolbox as nt
from nt_solutions import denoisingwav_6_curvelets as solutions
%matplotlib inline
%load_ext autoreload
%autoreload 2

n = 256
name = 'lena'
M = rescale(load_image(name, n))

options.null = 0
options.finest = 1
options.nbscales = 4
options.nbangles_coarse = 16
options.is_real = 1
options.n = n

MW = perform_curvelet_transform(M, options)

plot_curvelet(MW, options)

T = .2
MWT = perform_thresholding(MW, T, 'hard')

M1 = perform_curvelet_transform(MWT, options)

imageplot(M, 'Original', 1, 2, 1)
imageplot(clamp(M1), 'Approximated', 1, 2, 2)

name = 'lena'
n = 128
M0 = rescale(crop(load_image(name), n, [108 200]))
options.n = n

sigma = .05
M = M0 + sigma*randn(n)

solutions.exo1()

## Insert your code here.

imageplot(clamp(M), 'Noisy', 1, 2, 1)
imageplot(clamp(Mcurv), ['Denoised, SNR = ' num2str(snr(M0, Mcurv), 3) 'dB'], 1, 2, 2)

solutions.exo2()

## Insert your code here.

imageplot(clamp(M), 'Noisy', 1, 2, 1)
imageplot(clamp(Mcurv), ['Denoised, SNR = ' num2str(snr(M0, Mcurv), 3)], 1, 2, 2)

solutions.exo3()

## Insert your code here.

solutions.exo4()

## Insert your code here.