latent-diffusion/ldm/modules/image_degradation/bsrgan_light.py

# -*- coding: utf-8 -*-
import numpy as np
import cv2
import torch

from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations

import ldm.modules.image_degradation.utils_image as util

"""

# --------------------------------------------

# Super-Resolution

# --------------------------------------------

#

# Kai Zhang (cskaizhang@gmail.com)

# https://github.com/cszn

# From 2019/03--2021/08

# --------------------------------------------

"""


def modcrop_np(img, sf):
    '''

    Args:

        img: numpy image, WxH or WxHxC

        sf: scale factor

    Return:

        cropped image

    '''
    w, h = img.shape[:2]
    im = np.copy(img)
    return im[:w - w % sf, :h - h % sf, ...]


"""

# --------------------------------------------

# anisotropic Gaussian kernels

# --------------------------------------------

"""


def analytic_kernel(k):
    """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
    k_size = k.shape[0]
    # Calculate the big kernels size
    big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
    # Loop over the small kernel to fill the big one
    for r in range(k_size):
        for c in range(k_size):
            big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
    # Crop the edges of the big kernel to ignore very small values and increase run time of SR
    crop = k_size // 2
    cropped_big_k = big_k[crop:-crop, crop:-crop]
    # Normalize to 1
    return cropped_big_k / cropped_big_k.sum()


def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
    """ generate an anisotropic Gaussian kernel

    Args:

        ksize : e.g., 15, kernel size

        theta : [0,  pi], rotation angle range

        l1    : [0.1,50], scaling of eigenvalues

        l2    : [0.1,l1], scaling of eigenvalues

        If l1 = l2, will get an isotropic Gaussian kernel.

    Returns:

        k     : kernel

    """

    v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
    V = np.array([[v[0], v[1]], [v[1], -v[0]]])
    D = np.array([[l1, 0], [0, l2]])
    Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
    k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)

    return k


def gm_blur_kernel(mean, cov, size=15):
    center = size / 2.0 + 0.5
    k = np.zeros([size, size])
    for y in range(size):
        for x in range(size):
            cy = y - center + 1
            cx = x - center + 1
            k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)

    k = k / np.sum(k)
    return k


def shift_pixel(x, sf, upper_left=True):
    """shift pixel for super-resolution with different scale factors

    Args:

        x: WxHxC or WxH

        sf: scale factor

        upper_left: shift direction

    """
    h, w = x.shape[:2]
    shift = (sf - 1) * 0.5
    xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
    if upper_left:
        x1 = xv + shift
        y1 = yv + shift
    else:
        x1 = xv - shift
        y1 = yv - shift

    x1 = np.clip(x1, 0, w - 1)
    y1 = np.clip(y1, 0, h - 1)

    if x.ndim == 2:
        x = interp2d(xv, yv, x)(x1, y1)
    if x.ndim == 3:
        for i in range(x.shape[-1]):
            x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)

    return x


def blur(x, k):
    '''

    x: image, NxcxHxW

    k: kernel, Nx1xhxw

    '''
    n, c = x.shape[:2]
    p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
    x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
    k = k.repeat(1, c, 1, 1)
    k = k.view(-1, 1, k.shape[2], k.shape[3])
    x = x.view(1, -1, x.shape[2], x.shape[3])
    x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
    x = x.view(n, c, x.shape[2], x.shape[3])

    return x


def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
    """"

    # modified version of https://github.com/assafshocher/BlindSR_dataset_generator

    # Kai Zhang

    # min_var = 0.175 * sf  # variance of the gaussian kernel will be sampled between min_var and max_var

    # max_var = 2.5 * sf

    """
    # Set random eigen-vals (lambdas) and angle (theta) for COV matrix
    lambda_1 = min_var + np.random.rand() * (max_var - min_var)
    lambda_2 = min_var + np.random.rand() * (max_var - min_var)
    theta = np.random.rand() * np.pi  # random theta
    noise = -noise_level + np.random.rand(*k_size) * noise_level * 2

    # Set COV matrix using Lambdas and Theta
    LAMBDA = np.diag([lambda_1, lambda_2])
    Q = np.array([[np.cos(theta), -np.sin(theta)],
                  [np.sin(theta), np.cos(theta)]])
    SIGMA = Q @ LAMBDA @ Q.T
    INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]

    # Set expectation position (shifting kernel for aligned image)
    MU = k_size // 2 - 0.5 * (scale_factor - 1)  # - 0.5 * (scale_factor - k_size % 2)
    MU = MU[None, None, :, None]

    # Create meshgrid for Gaussian
    [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
    Z = np.stack([X, Y], 2)[:, :, :, None]

    # Calcualte Gaussian for every pixel of the kernel
    ZZ = Z - MU
    ZZ_t = ZZ.transpose(0, 1, 3, 2)
    raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)

    # shift the kernel so it will be centered
    # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)

    # Normalize the kernel and return
    # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
    kernel = raw_kernel / np.sum(raw_kernel)
    return kernel


def fspecial_gaussian(hsize, sigma):
    hsize = [hsize, hsize]
    siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
    std = sigma
    [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
    arg = -(x * x + y * y) / (2 * std * std)
    h = np.exp(arg)
    h[h < scipy.finfo(float).eps * h.max()] = 0
    sumh = h.sum()
    if sumh != 0:
        h = h / sumh
    return h


def fspecial_laplacian(alpha):
    alpha = max([0, min([alpha, 1])])
    h1 = alpha / (alpha + 1)
    h2 = (1 - alpha) / (alpha + 1)
    h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
    h = np.array(h)
    return h


def fspecial(filter_type, *args, **kwargs):
    '''

    python code from:

    https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py

    '''
    if filter_type == 'gaussian':
        return fspecial_gaussian(*args, **kwargs)
    if filter_type == 'laplacian':
        return fspecial_laplacian(*args, **kwargs)


"""

# --------------------------------------------

# degradation models

# --------------------------------------------

"""


def bicubic_degradation(x, sf=3):
    '''

    Args:

        x: HxWxC image, [0, 1]

        sf: down-scale factor

    Return:

        bicubicly downsampled LR image

    '''
    x = util.imresize_np(x, scale=1 / sf)
    return x


def srmd_degradation(x, k, sf=3):
    ''' blur + bicubic downsampling

    Args:

        x: HxWxC image, [0, 1]

        k: hxw, double

        sf: down-scale factor

    Return:

        downsampled LR image

    Reference:

        @inproceedings{zhang2018learning,

          title={Learning a single convolutional super-resolution network for multiple degradations},

          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},

          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},

          pages={3262--3271},

          year={2018}

        }

    '''
    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')  # 'nearest' | 'mirror'
    x = bicubic_degradation(x, sf=sf)
    return x


def dpsr_degradation(x, k, sf=3):
    ''' bicubic downsampling + blur

    Args:

        x: HxWxC image, [0, 1]

        k: hxw, double

        sf: down-scale factor

    Return:

        downsampled LR image

    Reference:

        @inproceedings{zhang2019deep,

          title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},

          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},

          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},

          pages={1671--1681},

          year={2019}

        }

    '''
    x = bicubic_degradation(x, sf=sf)
    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
    return x


def classical_degradation(x, k, sf=3):
    ''' blur + downsampling

    Args:

        x: HxWxC image, [0, 1]/[0, 255]

        k: hxw, double

        sf: down-scale factor

    Return:

        downsampled LR image

    '''
    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
    # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
    st = 0
    return x[st::sf, st::sf, ...]


def add_sharpening(img, weight=0.5, radius=50, threshold=10):
    """USM sharpening. borrowed from real-ESRGAN

    Input image: I; Blurry image: B.

    1. K = I + weight * (I - B)

    2. Mask = 1 if abs(I - B) > threshold, else: 0

    3. Blur mask:

    4. Out = Mask * K + (1 - Mask) * I

    Args:

        img (Numpy array): Input image, HWC, BGR; float32, [0, 1].

        weight (float): Sharp weight. Default: 1.

        radius (float): Kernel size of Gaussian blur. Default: 50.

        threshold (int):

    """
    if radius % 2 == 0:
        radius += 1
    blur = cv2.GaussianBlur(img, (radius, radius), 0)
    residual = img - blur
    mask = np.abs(residual) * 255 > threshold
    mask = mask.astype('float32')
    soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)

    K = img + weight * residual
    K = np.clip(K, 0, 1)
    return soft_mask * K + (1 - soft_mask) * img


def add_blur(img, sf=4):
    wd2 = 4.0 + sf
    wd = 2.0 + 0.2 * sf

    wd2 = wd2/4
    wd = wd/4

    if random.random() < 0.5:
        l1 = wd2 * random.random()
        l2 = wd2 * random.random()
        k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
    else:
        k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random())
    img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')

    return img


def add_resize(img, sf=4):
    rnum = np.random.rand()
    if rnum > 0.8:  # up
        sf1 = random.uniform(1, 2)
    elif rnum < 0.7:  # down
        sf1 = random.uniform(0.5 / sf, 1)
    else:
        sf1 = 1.0
    img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
    img = np.clip(img, 0.0, 1.0)

    return img


# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
#     noise_level = random.randint(noise_level1, noise_level2)
#     rnum = np.random.rand()
#     if rnum > 0.6:  # add color Gaussian noise
#         img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
#     elif rnum < 0.4:  # add grayscale Gaussian noise
#         img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
#     else:  # add  noise
#         L = noise_level2 / 255.
#         D = np.diag(np.random.rand(3))
#         U = orth(np.random.rand(3, 3))
#         conv = np.dot(np.dot(np.transpose(U), D), U)
#         img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
#     img = np.clip(img, 0.0, 1.0)
#     return img

def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
    noise_level = random.randint(noise_level1, noise_level2)
    rnum = np.random.rand()
    if rnum > 0.6:  # add color Gaussian noise
        img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
    elif rnum < 0.4:  # add grayscale Gaussian noise
        img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
    else:  # add  noise
        L = noise_level2 / 255.
        D = np.diag(np.random.rand(3))
        U = orth(np.random.rand(3, 3))
        conv = np.dot(np.dot(np.transpose(U), D), U)
        img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
    img = np.clip(img, 0.0, 1.0)
    return img


def add_speckle_noise(img, noise_level1=2, noise_level2=25):
    noise_level = random.randint(noise_level1, noise_level2)
    img = np.clip(img, 0.0, 1.0)
    rnum = random.random()
    if rnum > 0.6:
        img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
    elif rnum < 0.4:
        img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
    else:
        L = noise_level2 / 255.
        D = np.diag(np.random.rand(3))
        U = orth(np.random.rand(3, 3))
        conv = np.dot(np.dot(np.transpose(U), D), U)
        img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
    img = np.clip(img, 0.0, 1.0)
    return img


def add_Poisson_noise(img):
    img = np.clip((img * 255.0).round(), 0, 255) / 255.
    vals = 10 ** (2 * random.random() + 2.0)  # [2, 4]
    if random.random() < 0.5:
        img = np.random.poisson(img * vals).astype(np.float32) / vals
    else:
        img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
        img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
        noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
        img += noise_gray[:, :, np.newaxis]
    img = np.clip(img, 0.0, 1.0)
    return img


def add_JPEG_noise(img):
    quality_factor = random.randint(80, 95)
    img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
    result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
    img = cv2.imdecode(encimg, 1)
    img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
    return img


def random_crop(lq, hq, sf=4, lq_patchsize=64):
    h, w = lq.shape[:2]
    rnd_h = random.randint(0, h - lq_patchsize)
    rnd_w = random.randint(0, w - lq_patchsize)
    lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]

    rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
    hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
    return lq, hq


def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
    """

    This is the degradation model of BSRGAN from the paper

    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"

    ----------

    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)

    sf: scale factor

    isp_model: camera ISP model

    Returns

    -------

    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]

    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]

    """
    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
    sf_ori = sf

    h1, w1 = img.shape[:2]
    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
    h, w = img.shape[:2]

    if h < lq_patchsize * sf or w < lq_patchsize * sf:
        raise ValueError(f'img size ({h1}X{w1}) is too small!')

    hq = img.copy()

    if sf == 4 and random.random() < scale2_prob:  # downsample1
        if np.random.rand() < 0.5:
            img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
                             interpolation=random.choice([1, 2, 3]))
        else:
            img = util.imresize_np(img, 1 / 2, True)
        img = np.clip(img, 0.0, 1.0)
        sf = 2

    shuffle_order = random.sample(range(7), 7)
    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
    if idx1 > idx2:  # keep downsample3 last
        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]

    for i in shuffle_order:

        if i == 0:
            img = add_blur(img, sf=sf)

        elif i == 1:
            img = add_blur(img, sf=sf)

        elif i == 2:
            a, b = img.shape[1], img.shape[0]
            # downsample2
            if random.random() < 0.75:
                sf1 = random.uniform(1, 2 * sf)
                img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
                                 interpolation=random.choice([1, 2, 3]))
            else:
                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
                k_shifted = shift_pixel(k, sf)
                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
                img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
                img = img[0::sf, 0::sf, ...]  # nearest downsampling
            img = np.clip(img, 0.0, 1.0)

        elif i == 3:
            # downsample3
            img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
            img = np.clip(img, 0.0, 1.0)

        elif i == 4:
            # add Gaussian noise
            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8)

        elif i == 5:
            # add JPEG noise
            if random.random() < jpeg_prob:
                img = add_JPEG_noise(img)

        elif i == 6:
            # add processed camera sensor noise
            if random.random() < isp_prob and isp_model is not None:
                with torch.no_grad():
                    img, hq = isp_model.forward(img.copy(), hq)

    # add final JPEG compression noise
    img = add_JPEG_noise(img)

    # random crop
    img, hq = random_crop(img, hq, sf_ori, lq_patchsize)

    return img, hq


# todo no isp_model?
def degradation_bsrgan_variant(image, sf=4, isp_model=None):
    """

    This is the degradation model of BSRGAN from the paper

    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"

    ----------

    sf: scale factor

    isp_model: camera ISP model

    Returns

    -------

    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]

    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]

    """
    image = util.uint2single(image)
    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
    sf_ori = sf

    h1, w1 = image.shape[:2]
    image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
    h, w = image.shape[:2]

    hq = image.copy()

    if sf == 4 and random.random() < scale2_prob:  # downsample1
        if np.random.rand() < 0.5:
            image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
                               interpolation=random.choice([1, 2, 3]))
        else:
            image = util.imresize_np(image, 1 / 2, True)
        image = np.clip(image, 0.0, 1.0)
        sf = 2

    shuffle_order = random.sample(range(7), 7)
    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
    if idx1 > idx2:  # keep downsample3 last
        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]

    for i in shuffle_order:

        if i == 0:
            image = add_blur(image, sf=sf)

        # elif i == 1:
        #     image = add_blur(image, sf=sf)

        if i == 0:
            pass

        elif i == 2:
            a, b = image.shape[1], image.shape[0]
            # downsample2
            if random.random() < 0.8:
                sf1 = random.uniform(1, 2 * sf)
                image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
                                   interpolation=random.choice([1, 2, 3]))
            else:
                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
                k_shifted = shift_pixel(k, sf)
                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
                image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
                image = image[0::sf, 0::sf, ...]  # nearest downsampling

            image = np.clip(image, 0.0, 1.0)

        elif i == 3:
            # downsample3
            image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
            image = np.clip(image, 0.0, 1.0)

        elif i == 4:
            # add Gaussian noise
            image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2)

        elif i == 5:
            # add JPEG noise
            if random.random() < jpeg_prob:
                image = add_JPEG_noise(image)
        #
        # elif i == 6:
        #     # add processed camera sensor noise
        #     if random.random() < isp_prob and isp_model is not None:
        #         with torch.no_grad():
        #             img, hq = isp_model.forward(img.copy(), hq)

    # add final JPEG compression noise
    image = add_JPEG_noise(image)
    image = util.single2uint(image)
    example = {"image": image}
    return example




if __name__ == '__main__':
    print("hey")
    img = util.imread_uint('utils/test.png', 3)
    img = img[:448, :448]
    h = img.shape[0] // 4
    print("resizing to", h)
    sf = 4
    deg_fn = partial(degradation_bsrgan_variant, sf=sf)
    for i in range(20):
        print(i)
        img_hq = img
        img_lq = deg_fn(img)["image"]
        img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq)
        print(img_lq)
        img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)["image"]
        print(img_lq.shape)
        print("bicubic", img_lq_bicubic.shape)
        print(img_hq.shape)
        lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
                                interpolation=0)
        lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic),
                                        (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
                                        interpolation=0)
        img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
        util.imsave(img_concat, str(i) + '.png')