Args: x: HxWxC image, [0, 1] sf: down-scale factor Return: bicubicly downsampled LR image

python code from: https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_fil

Get human readable file size. Args: size (int): File size. suffix (str): Suffix. Default: 'B'. Return: str: Forma

Args: model: trained model L: input Low-quality image refield: effective receptive filed of the network, 32 is enough

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) > thre

generate an anisotropic Gaussian kernel Args: ksize : e.g., 15, kernel size theta : [0, pi], rotation angle range l1

Kai Zhang (github: https://github.com/cszn)

bgr version of rgb2ycbcr only_y: only return Y channel Input: uint8, [0, 255] float, [0, 1]

This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"

Download files from google drive. Ref: https://stackoverflow.com/questions/25010369/wget-curl-large-file-from-google-drive # noqa E501

imgs: list, N images of size WxHxC

shift pixel for super-resolution with different scale factors Args: x: WxHxC or WxH sf: scale factor upper_left: shift

# --------------------------------------- # Kai Zhang (github: https://github.com/cszn) # 03/Mar/2019 # -------------------------

Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)

Kai Zhang (github: https://github.com/cszn)

Kai Zhang (github: https://github.com/cszn)

x: image, NxcxHxW k: kernel, Nx1xhxw

calculate SSIM the same outputs as MATLAB's img1, img2: [0, 255]

blur + downsampling Args: x: HxWxC image, [0, 1]/[0, 255] k: hxw, double sf: down-scale factor Return:

This is an extended degradation model by combining the degradation models of BSRGAN and Real-ESRGAN ---------- img: HXWXC, [0, 1]

bicubic downsampling + blur Args: x: HxWxC image, [0, 1] k: hxw, double sf: down-scale factor Return:

# --------------------------------------- # Kai Zhang (github: https://github.com/cszn) # 03/Mar/2019 # -------------------------

# modified version of https://github.com/assafshocher/BlindSR_dataset_generator # Kai Zhang # min_var = 0.175 * sf # variance of the

set up logger modified by Kai Zhang (github: https://github.com/cszn)

Args: img: numpy image, WxH or WxHxC sf: scale factor Return: cropped image

same as matlab rgb2ycbcr only_y: only return Y channel Input: uint8, [0, 255] float, [0, 1]

split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size), and save them into taget_dataroot; o

blur + bicubic downsampling Args: x: HxWxC image, [0, 1] k: hxw, double sf: down-scale factor Return: