A cross Transformer for image denoising
Chunwei Tian Menghua Zheng Wangmeng Zuo Shichao Zhang Yanning Zhang Chia-Wen Ling
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.