How to generate local noise feature using RGB input?

[fei]

Sorry but I can't find the implementation code of the noise input using SRM filter?

I write one using pytorch but the output seem different from paper, here's the code.

import torch
from torch.nn.functional import conv2d
from torch.nn import Conv2d
import numpy as np


def srm_filter(x):
    q = [4.0, 12.0, 2.0]
    filter1 = [[0, 0, 0, 0, 0],
               [0, -1, 2, -1, 0],
               [0, 2, -4, 2, 0],
               [0, -1, 2, -1, 0],
               [0, 0, 0, 0, 0]]
    filter2 = [[-1, 2, -2, 2, -1],
               [2, -6, 8, -6, 2],
               [-2, 8, -12, 8, -2],
               [2, -6, 8, -6, 2],
               [-1, 2, -2, 2, -1]]
    filter3 = [[0, 0, 0, 0, 0],
               [0, 0, 0, 0, 0],
               [0, 1, -2, 1, 0],
               [0, 0, 0, 0, 0],
               [0, 0, 0, 0, 0]]
    filter1 = np.asarray(filter1, dtype=np.float32) / q[0]
    filter2 = np.asarray(filter2, dtype=np.float32) / q[1]
    filter3 = np.asarray(filter3, dtype=np.float32) / q[2]
    filters = np.asarray(
        [
            [filter1, filter2, filter3],    # R
            [filter1, filter2, filter3],    # G
            [filter1, filter2, filter3],    # B
        ], dtype=np.float32)

    output = conv2d(torch.from_numpy(x.astype(np.float32)), torch.from_numpy(filters), stride=1, padding=2)

    return output


if __name__ == '__main__':
    from PIL import Image

    raw_img = Image.open(
        r'/project/scene_classify/test_images/截屏2020-07-07 下午9.52.22.png').convert('RGB')

    noisemap = srm_filter((np.asarray(raw_img) / 255.0).transpose(2, 0, 1)[np.newaxis, ...])

    noisemap = Image.fromarray((noisemap.squeeze(dim=0).numpy() * 255.0).transpose(1, 2, 0).astype(np.uint8))
    noisemap.save('noise_map.png')
    raw_img.save('raw_map.png')

and here's the output:

the output from paper:

[zj56]

import torch
from torch.nn.functional import conv2d
from torch.nn import Conv2d
import numpy as np

def srm_filter(x):
q = [4.0, 12.0, 2.0]
filter1 = [[0, 0, 0, 0, 0],
[0, -1, 2, -1, 0],
[0, 2, -4, 2, 0],
[0, -1, 2, -1, 0],
[0, 0, 0, 0, 0]]
filter2 = [[-1, 2, -2, 2, -1],
[2, -6, 8, -6, 2],
[-2, 8, -12, 8, -2],
[2, -6, 8, -6, 2],
[-1, 2, -2, 2, -1]]
filter3 = [[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 1, -2, 1, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]]
filter1 = np.asarray(filter1, dtype=np.float32) / q[0]
filter2 = np.asarray(filter2, dtype=np.float32) / q[1]
filter3 = np.asarray(filter3, dtype=np.float32) / q[2]
filters = np.asarray(
[
[filter3, filter3, filter3], # R
[filter2, filter2, filter2], # G
[filter1, filter1, filter1], # B
], dtype=np.float32)

output = conv2d(torch.from_numpy(x.astype(np.float32)), torch.from_numpy(filters), stride=1, padding=2)

return output

if name == 'main':
from PIL import Image

raw_img = Image.open(
    r'./2.png').convert('RGB')

noisemap = srm_filter((np.asarray(raw_img) / 255.0).transpose(2, 0, 1)[np.newaxis, ...])

noisemap = Image.fromarray((noisemap.squeeze(dim=0).numpy() * 255.0).transpose(1, 2, 0).astype(np.uint8))
noisemap.save('noise_map.png')
raw_img.save('raw_map.png')