Calculate SSIM (structural similarity) for one channel images. It is called by func:`calculate_ssim`. Args: img1 (ndarray): Images w

horizontal flip OR rotate (0, 90, 180, 270 degrees)

horizontal flip OR rotate (0, 90, 180, 270 degrees) with flows

horizontal flip OR rotate (0, 90, 180, 270 degrees)

Bundles submission data for raw denoising submission_folder Folder where denoised images reside Output is written to <submission_folder

Bundles submission data for sRGB denoising submission_folder Folder where denoised images reside Output is written to <submission_f

Bundles submission data for sRGB denoising submission_folder Folder where denoised images reside Output is written to <submission_f

Numpy implementation of the Frechet Distance. The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) and X_2 ~ N(mu_2, C_

Calculate NIQE (Natural Image Quality Evaluator) metric. Ref: Making a "Completely Blind" Image Quality Analyzer. This implementation could p

Calculate PSNR (Peak Signal-to-Noise Ratio). Ref: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio Args: img1 (ndarray/tenso

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

Calculate SSIM (structural similarity). Ref: Image quality assessment: From error visibility to structural similarity The results are th

get the image data and update pbar.

conversion among BGR, gray and y

Crop borders of images. Args: imgs (list[ndarray] | ndarray): Images with shape (h, w, c). crop_border (int): Crop border for eac

Args: img_path (str): Image path. upsample_num_times (int): Upsamples the image before running the fa

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

Extract inception features. Args: data_generator (generator): A data generator. inception (nn.Module): Inception model. l

Warp an image or feature map with optical flow. Args: x (Tensor): Tensor with size (n, c, h, w). flow (Tensor): Tensor with size

Read an optical flow map. Args: flow_path (ndarray or str): Flow path. quantize (bool): whether to read quantized pair, if set to

Write optical flow to file. If the flow is not quantized, it will be saved as a .flo file losslessly, otherwise a jpeg image which is lossy b

x_in: [b,h,w,c] # input_feature illu_fea: [b,h,w,c] # mask shift? 为什么是 b, h, w, c? return out: [b,h,w,c]

x: [b,h,w,c] return out: [b,h,w,c]

x: [b,c,h,w] illu_fea: [b,c,h,w] return out: [b,c,h,w]

x: [b,c,h,w] x是feature, 不是image illu_fea: [b,c,h,w] return out: [b,c,h,w]

x: [b,c,h,w] return out:[b,c,h,w]

x_in: [b,h,w,c] return out: [b,h,w,c]

x: [b,h,w,c] return out: [b,h,w,c]

x: [b,c,h,w] return out: [b,c,h,w]

x: [b,c,h,w] return out:[b,c,h,w]

x: [b,c,h,w] return out:[b,c,h,w]

Args: pred (Tensor): of shape (N, C, H, W). Predicted tensor. target (Tensor): of shape (N, C, H, W). Ground truth te

Args: pred (Tensor): of shape (N, C, H, W). Predicted tensor. target (Tensor): of shape (N, C, H, W). Ground truth te