This paper as well as A cross Transformer for image denoising is conducted by Chunwei Tian, Menghua Zheng, Wangmeng Zuo, Shichao Zhang, Yanning Zhang, Chia-Wen Lin. This paper is accepted by the Information Fusion (IF:18.6)(https://www.sciencedirect.com/science/article/abs/pii/S1566253523003597) or https://arxiv.org/pdf/2310.10408.pdf. And it is implemented by Pytorch. Besides, it is reported by famous computer vision platform of 52CV at https://mp.weixin.qq.com/s/Debh7PZSFTBtOVxpFh9yfQ .
Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to obtain good performance in image denoising. However, how to obtain effective structural information via CNNs to efficiently represent given noisy images is key for complex scenes. In this paper, we propose a cross Transformer denoising CNN (CTNet) with a serial block (SB), a parallel block (PB), and a residual block (RB) to obtain clean images for complex scenes. A SB uses an enhanced residual architecture to deeply search structural information for image denoising. To avoid loss of key information, PB uses three heterogeneous networks to implement multiple interactions of multi-level features to broadly search for extra information for improving the adaptability of an obtained denoiser for complex scenes. Also, to improve denoising performance, Transformer mechanisms are embedded into the SB and PB to extract complementary salient features for effectively removing noise in terms of pixel relations. Finally, a RB is applied to acquire clean images. Experiments illustrate that our CTNet is superior to some popular denoising methods in terms of real and synthetic image denoising. It is suitable to mobile digital devices, i.e., phones. Codes can be obtained at https://github.com/hellloxiaotian/CTNet.
The training dataset of the CBSD500 images is downloaded at https://pan.baidu.com/s/1ou2mK5JUh-K8iMu8-DMcMw (baiduyun) or https://drive.google.com/open?id=1S1_QrP-fIXeFl5hYY193lr07KyZV8X8r (google drive)
The training dataset of the DIV2K can be obtained at https://data.vision.ee.ethz.ch/cvl/DIV2K/
The training dataset of the Flickr2K can be obtained at http://cv.snu.ac.kr/research/EDSR/Flickr2K.tar
The training dataset of the WED can be obtained at https://ece.uwaterloo.ca/~k29ma/exploration/
Test dataset of BSD68 is downloaded at https://drive.google.com/file/d/1_fw6EKne--LVnW0mo68RrIY-j6BKPdSp/view?usp=sharing (google drive)
Test dataset of Set12 is downloaded at https://drive.google.com/file/d/1cpQwFpNv1MXsM5bJkIumYfww8EPtlkWf/view?usp=sharing (google drive)
Test dataset of CBSD68 is downloaded at https://drive.google.com/file/d/1lxXQ_buMll_JVWxKpk5fp0jduW5F_MHe/view?usp=sharing (google drive)
Test dataset of Kodak24 is downloaded at https://drive.google.com/file/d/1F4_mv4oTXhiG-zyG9DI4OO05KqvEKhs9/view?usp=sharing (google drive)
The training dataset of real noisy images is downloaded at https://drive.google.com/file/d/1IYkR4zi76p7O5OCevC11VaQeKx0r1GyT/view?usp=sharing and https://drive.google.com/file/d/19MA-Rgfc89sW9GJHpj_QedFyo-uoS8o7/view?usp=sharing (google drive)
The test dataset of real noisy images is downloaded at https://drive.google.com/file/d/17DE-SV85Slu2foC0F0Ftob5VmRrHWI2h/view?usp=sharing (google drive)
Download model file at https://pan.baidu.com/s/1HX1EqfvUbAvSEJCxkAtX_A (passwd: 0hbf)
cd main
python trian.py --mode test --model_name gtd --rgb_range 1 --n_colors 1 --sigma 25 --num_layers 1 --n_GPUs 1 --GPU_id 3 --test_dataset Set12 --model_file_name ../model_zoo/ctnet/g25/model_sigma25.pth
python trian.py --mode test --model_name gtd --rgb_range 1 --n_colors 1 --sigma 50 --num_layers 1 --n_GPUs 1 --GPU_id 3 --test_dataset Set12 --model_file_name ../model_zoo/ctnet/denoising_b/g/model_sigma100.pth
python trian.py --mode test --model_name gtd --rgb_range 1 --n_colors 3 --sigma 25 --num_layers 1 --n_GPUs 1 --GPU_id 3 --test_dataset CBSD68 --model_file_name ../model_zoo/ctnet/c25/model_sigma25.pth
python trian.py --mode test --model_name gtd --rgb_range 1 --n_colors 3 --sigma 50 --num_layers 1 --n_GPUs 1 --GPU_id 3 --test_dataset CBSD68 --model_file_name ../model_zoo/ctnet/denoising_b/c/model_sigma100.pth
cd main
python trian.py --mode train --n_pat_per_image 48 --batch_size 8 --lr 0.0002 --model_name gtd --rgb_range 1 --n_colors 1 --sigma 25 --num_layers 1 --n_GPUs 1 --GPU_id 0