Noise removal of images is an essential preprocessing procedure for many computer vision tasks. Currently, many denoising models based on deep neural networks can perform well in removing the noise with known distributions (i.e. the additive ...

In image denoising, deep convolutional neural networks (CNNs) can obtain favorable performance on removing spatially invariant noise. However, many of these networks cannot perform well on removing the real noise (i.e. spatially variant noise) ...

• A novel dual residual attention network (DRANet) is designed for image blind denoising, which is effective for both the synthetic noise and real-world noise.
• Two types of residual attention blocks (RDAB and HDRAB) are proposed for the ...

At present, transformer-based target tracking algorithms mainly use transformers to fuse deep convolution features, their tracking accuracy is competitive, however compared with convolutional neural networks, their tracking speed is slow. Due to the ...