#!/usr/bin/python
import numpy as np
class NLM:
'Non-Local Means Denoising'
def __init__(self, img, ds, Ds, h, clip):
self.img = img
self.ds = ds # neighbour window size - 1 /2
self.Ds = Ds # search window size - 1 / 2
self.h = h
self.clip = clip
def padding(self):
img_pad = np.pad(self.img, (self.Ds, self.Ds), 'reflect')
return img_pad
def clipping(self):
np.clip(self.img, 0, self.clip, out=self.img)
return self.img
def calWeights(self, img, kernel, y, x):
wmax = 0
sweight = 0
average = 0
for j in range(2 * self.Ds + 1 - 2 * self.ds - 1):
for i in range(2 * self.Ds + 1 - 2 * self.ds - 1):
start_y = y - self.Ds + self.ds + j
start_x = x - self.Ds + self.ds + i
neighbour_w = img[start_y - self.ds:start_y + self.ds + 1, start_x - self.ds:start_x + self.ds + 1]
center_w = img[y-self.ds:y+self.ds+1, x-self.ds:x+self.ds+1]
if j != y or i != x:
sub = np.subtract(neighbour_w, center_w)
dist = np.sum(np.multiply(kernel, np.multiply(sub, sub)))
w = np.exp(-dist/pow(self.h, 2)) # replaced by look up table
if w > wmax:
wmax = w
sweight = sweight + w
average = average + w * img[start_y, start_x]
return sweight, average, wmax
def execute(self):
img_pad = self.padding()
img_pad = img_pad.astype(np.uint16)
raw_h = self.img.shape[0]
raw_w = self.img.shape[1]
nlm_img = np.empty((raw_h, raw_w), np.uint16)
kernel = np.ones((2*self.ds+1, 2*self.ds+1)) / pow(2*self.ds+1, 2)
for y in range(img_pad.shape[0] - 2 * self.Ds):
for x in range(img_pad.shape[1] - 2 * self.Ds):
center_y = y + self.Ds
center_x = x + self.Ds
sweight, average, wmax = self.calWeights(img_pad, kernel, center_y, center_x)
average = average + wmax * img_pad[center_y, center_x]
sweight = sweight + wmax
nlm_img[y,x] = average / sweight
self.img = nlm_img
return self.clipping()
