third_party/DarkFeat/datasets/noise.py

import numpy as np
import random
from scipy.stats import tukeylambda

camera_params = {
    "Kmin": 0.2181895124454343,
    "Kmax": 3.0,
    "G_shape": np.array(
        [
            0.15714286,
            0.14285714,
            0.08571429,
            0.08571429,
            0.2,
            0.2,
            0.1,
            0.08571429,
            0.05714286,
            0.07142857,
            0.02857143,
            0.02857143,
            0.01428571,
            0.02857143,
            0.08571429,
            0.07142857,
            0.11428571,
            0.11428571,
        ]
    ),
    "Profile-1": {
        "R_scale": {
            "slope": 0.4712797750747537,
            "bias": -0.8078958947116487,
            "sigma": 0.2436176299944695,
        },
        "g_scale": {
            "slope": 0.6771267783987617,
            "bias": 1.5121876510805845,
            "sigma": 0.24641096601611254,
        },
        "G_scale": {
            "slope": 0.6558756156508007,
            "bias": 1.09268679594838,
            "sigma": 0.28604721742277756,
        },
    },
    "black_level": 2048,
    "max_value": 16383,
}


# photon shot noise
def addPStarNoise(img, K):
    return np.random.poisson(img / K).astype(np.float32) * K


# read noise
# tukey lambda distribution
def addGStarNoise(img, K, G_shape, G_scale_param):
    # sample a shape parameter [lambda] from histogram of samples
    a, b = np.histogram(G_shape, bins=10, range=(-0.25, 0.25))
    a, b = np.array(a), np.array(b)
    a = a / a.sum()

    rand_num = random.uniform(0, 1)
    idx = np.sum(np.cumsum(a) < rand_num)
    lam = random.uniform(b[idx], b[idx + 1])

    # calculate scale parameter [G_scale]
    log_K = np.log(K)
    log_G_scale = (
        np.random.standard_normal() * G_scale_param["sigma"] * 1
        + G_scale_param["slope"] * log_K
        + G_scale_param["bias"]
    )
    G_scale = np.exp(log_G_scale)
    # print(f'G_scale: {G_scale}')

    return img + tukeylambda.rvs(lam, scale=G_scale, size=img.shape).astype(np.float32)


# row noise
# uniform distribution for each row
def addRowNoise(img, K, R_scale_param):
    # calculate scale parameter [R_scale]
    log_K = np.log(K)
    log_R_scale = (
        np.random.standard_normal() * R_scale_param["sigma"] * 1
        + R_scale_param["slope"] * log_K
        + R_scale_param["bias"]
    )
    R_scale = np.exp(log_R_scale)
    # print(f'R_scale: {R_scale}')

    row_noise = np.random.randn(img.shape[0], 1).astype(np.float32) * R_scale
    return img + np.tile(row_noise, (1, img.shape[1]))


# quantization noise
# uniform distribution
def addQuantNoise(img, q):
    return img + np.random.uniform(low=-0.5 * q, high=0.5 * q, size=img.shape)


def sampleK(Kmin, Kmax):
    return np.exp(np.random.uniform(low=np.log(Kmin), high=np.log(Kmax)))