马赛克想必大家都见过，但往往大家都非常好奇马赛克后面的内容，那么我们要怎么去除呢？

下面是代码片段

from PULSE import PULSE from torch.utils.data import Dataset, DataLoader from torch.nn import DataParallel from pathlib import Path from PIL import Image import torchvision from math import log10, ceil import argparse class Images(Dataset): def __init__(self, root_dir, duplicates): self.root_path = Path(root_dir) self.image_list = list(self.root_path.glob("*.png")) self.duplicates = duplicates # Number of times to duplicate the image in the dataset to produce multiple HR images def __len__(self): return self.duplicates*len(self.image_list) def __getitem__(self, idx): img_path = self.image_list[idx//self.duplicates] image = torchvision.transforms.ToTensor()(Image.open(img_path)) if(self.duplicates == 1): return image,img_path.stem else: return image,img_path.stem+f"_{(idx % self.duplicates)+1}" parser = argparse.ArgumentParser(description='PULSE') #I/O arguments parser.add_argument('-input_dir', type=str, default='input', help='input data directory') parser.add_argument('-output_dir', type=str, default='runs', help='output data directory') parser.add_argument('-cache_dir', type=str, default='cache', help='cache directory for model weights') parser.add_argument('-duplicates', type=int, default=1, help='How many HR images to produce for every image in the input directory') parser.add_argument('-batch_size', type=int, default=1, help='Batch size to use during optimization') #PULSE arguments parser.add_argument('-seed', type=int, help='manual seed to use') parser.add_argument('-loss_str', type=str, default="100*L2+0.05*GEOCROSS", help='Loss function to use') parser.add_argument('-eps', type=float, default=2e-3, help='Target for downscaling loss (L2)') parser.add_argument('-noise_type', type=str, default='trainable', help='zero, fixed, or trainable') parser.add_argument('-num_trainable_noise_layers', type=int, default=5, help='Number of noise layers to optimize') parser.add_argument('-tile_latent', action='store_true', help='Whether to forcibly tile the same latent 18 times') parser.add_argument('-bad_noise_layers', type=str, default="17", help='List of noise layers to zero out to improve image quality') parser.add_argument('-opt_name', type=str, default='adam', help='Optimizer to use in projected gradient descent') parser.add_argument('-learning_rate', type=float, default=0.4, help='Learning rate to use during optimization') parser.add_argument('-steps', type=int, default=100, help='Number of optimization steps') parser.add_argument('-lr_schedule', type=str, default='linear1cycledrop', help='fixed, linear1cycledrop, linear1cycle') parser.add_argument('-save_intermediate', action='store_true', help='Whether to store and save intermediate HR and LR images during optimization') kwargs = vars(parser.parse_args()) dataset = Images(kwargs["input_dir"], duplicates=kwargs["duplicates"]) out_path = Path(kwargs["output_dir"]) out_path.mkdir(parents=True, exist_ok=True) dataloader = DataLoader(dataset, batch_size=kwargs["batch_size"]) model = PULSE(cache_dir=kwargs["cache_dir"]) model = DataParallel(model) toPIL = torchvision.transforms.ToPILImage() for ref_im, ref_im_name in dataloader: if(kwargs["save_intermediate"]): padding = ceil(log10(100)) for i in range(kwargs["batch_size"]): int_path_HR = Path(out_path / ref_im_name[i] / "HR") int_path_LR = Path(out_path / ref_im_name[i] / "LR") int_path_HR.mkdir(parents=True, exist_ok=True) int_path_LR.mkdir(parents=True, exist_ok=True) for j,(HR,LR) in enumerate(model(ref_im,kwargs)): for i in range(kwargs["batch_size"]): toPIL(HR[i].cpu().detach().clamp(0, 1)).save( int_path_HR / f"{ref_im_name[i]}_{j:0{padding}}.png") toPIL(LR[i].cpu().detach().clamp(0, 1)).save( int_path_LR / f"{ref_im_name[i]}_{j:0{padding}}.png") else: #out_im = model(ref_im,kwargs) for j,(HR,LR) in enumerate(model(ref_im,kwargs)): for i in range(kwargs["batch_size"]): toPIL(HR[i].cpu().detach().clamp(0, 1)).save( out_path / f"{ref_im_name[i]}.png") 

用了PIL等库来实现了去除马赛克，代码不全，完整代码请转著页下载