The exposed cameras of UAV can shake, shift, or even malfunction under the influence of harsh weather, while the add-on devices (Dupont lines) are very vulnerable to damage. We can place a low-cost T-OLED overlay around the camera to protect it, but this would also introduce image degradation issues. In particular, the temperature variations in the atmosphere can create mist that adsorbs to the T-OLED, which can cause secondary disasters (i.e., more severe image degradation) during the UAV’s filming process. To solve the image degradation problem caused by overlaying T-OLEDs, in this paper we propose a new method to enhance the visual experience by enhancing the texture and color of images. Specifically, our method trains a lightweight network to estimate a low-rank affine grid on the input image, and then utilizes the grid to enhance the input image at block granularity. The advantages of our method are that no reference image is required and the loss function is developed from visual experience. In addition, our model can perform high-quality recovery of images of arbitrary resolution in real time. In the end, the limitations of our model and the collected datasets (including the daytime and nighttime scenes) are discussed.
Cite: http://arxiv.org/abs/2202.06283
Video dataset : Link：https://pan.baidu.com/s/1vMEZ3RmUOhiAKiTI_FyRjA Password： 3v5w
Checkpoint: ♥
Link: https://pan.baidu.com/s/1R0yLN1vixbbjRAdLDh74Sw Password: s2sr
Test results : Link: https://pan.baidu.com/s/1jpmZdUy7_RD0RMA5r1DdQw Password: n0qs
import torch
import torch.optim
from torchvision import transforms
from PIL import Image
import torch
from torchvision.utils import save_image
import net
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
my_model = net.Zero_Net().to(device)
my_model.eval()
my_model.to(device)
my_model.load_state_dict(torch.load("train_model_udc.pth", map_location=torch.device('cpu')))
to_pil_image = transforms.ToPILImage()
tfs_full = transforms.Compose([
transforms.ToTensor()
])
i = 0
for idx in range(1):
image_in = Image.open('3.jpg').convert('RGB')
full = tfs_full(image_in).unsqueeze(0).to(device)
output = my_model(full)
save_image(output[0], '{}.jpg'.format('3_test'))https://pytorch.org/docs/stable/quantization.html
Our model can completely replace bilateral grid learning in the field of image enhancement. ♥ Our approach can process images of arbitrary resolution on a single GPU. ♥
- https://github.com/google/hdrnet HDRNet
- https://github.com/mousecpn/Joint-Bilateral-Learning JBL approach
We have uploaded the required losses, of which DC is the most key. Readers can tune them according to the needs of their projects. Notably, gamma is a newly added loss that can also be pre-designed. We provide a simple sample. The key is that gamma correction is also needed, and each iteration allows itself to evolve.
https://github.com/bsun0802/Zero-DCE Loss functions on our approach.
https://github.com/yunlongdong/semi-dehazing Loss function on our approach.
https://github.com/rui1996/DeRaindrop Comparison experiments.