RCDNet: A Model-driven Deep Neural Network for Single Image Rain Removal (CVPR2020)

Related tags

Deep Learning RCDNet_simple
Overview

RCDNet: A Model-driven Deep Neural Network for Single Image Rain Removal (CVPR2020)

Hong Wang, Qi Xie, Qian Zhao, and Deyu Meng [PDF] [Supplementary Material]

The extension of this work is released as DRCDNet where we propose a dynamic rain kernel inference mechanism.

This is a simple coding framework, which has better compatibility with running environments. The original coding framework for RCDNet CVPR2020 is released at https://github.com/hongwang01/RCDNet

Dependicies

This repository is tested under the following system settings:

Python 3.6

Pytorch 1.4.0

CUDA 10.1

GPU NVIDIA Tesla V100-SMX2

Dataset

Please refer to RCDNet, CVPR2020 for downloading datasets and put them into the corresponding folders according to the folder dictionary in VRGNet, CVPR2021

Citation

@InProceedings{Wang_2020_CVPR,  
author = {Wang, Hong and Xie, Qi and Zhao, Qian and Meng, Deyu},  
title = {A Model-Driven Deep Neural Network for Single Image Rain Removal},  
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},  
month = {June},  
year = {2020}  
}

Contact

If you have any question, please feel free to concat Hong Wang (Email: [email protected])

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Comments
  • Possible mistakes in loss function

    Possible mistakes in loss function

    An iterative approach is adopted to calculate the sum of training losses at each stage. However, it's weird to use float() function here, since it would convert torch.Tensor to a scalar and hence break the gradient propagation chain. As a result, only the MSE loss of the last stage is involved in loss function.

    # train_main_syn.py
loss_Bs = 0
for j in range(opt.S):
    loss_Bs = float(loss_Bs) + 0.1 * F.mse_loss(ListB[j], im_gt)  # gradient propagation is breaked

    This can be verified by printing the gradient values.

    torch.manual_seed(1)

O, B = some_data()
output = net(O)

# uncomment the code below to test
# type 1, equivalent to the original code
from functools import reduce
loss = reduce(lambda acc, x: float(acc) + 0.1 * F.mse_loss(x, B), [0] + ListB[:opt.S])

# type 2, involving only the last stage, with the same gradient result as type 1
# loss = 0.1 * F.mse_loss(ListB[ops.S-1], B)

# type 3, cumulative loss at each stage
# loss = 0.1 * sum([F.mse_loss(x, B) for x in ListB[:opt.S]])

loss.backgward()
print(net.tau.grad)
    opened by biubiubiiu 1
Owner
Hong Wang
Natural Image Enhancement and Restoration, Medical Image Reconstruction, Image Processing, Joint Model-Driven and Data-Driven
Hong Wang
GitHub
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