Boosting has received considerable attention to improve the overall performance of model in multiple tasks by cascading many steerable sub-modules. In this paper, a boosting attention fusion generative adversarial network (BAF-GAN) was proposed, which allows boosting idea and attention mechanism modeling for high-quality image denoising. Specifically, several boosting module groups (BMGs) with group skip connection were employed to form denoiser. Each BMG contains some boosting attention fusion blocks (BAFBs). Each BAFB consists of parallel spatial attention unit and channel attention unit interleaved connection. Moreover, the multi-dimensional inner skip connection within BAFB can carry abundant informative features. Besides, spatial and channel attention mechanisms were also embedded in the discriminator to enhance its ability of discriminating various dimensional information. Meanwhile, a new loss function was given to assist the training process of the model. BAF-GAN can be applied to remove image noise, e.g., Gaussian noise and mixed noise. Comprehensive experiment results demonstrate that the BAF-GAN has the state-of-the-art performance.

通过级联许多可操纵的子模块，Boosting已经得到了相当多的关注，以提高模型在多个任务中的整体性能。本文提出了一种增强注意力融合的生成对抗网络（BAF-GAN），它可以为高质量的图像去噪提供增强思想和注意力机制的模型。具体而言，采用具有组跳过连接的几个增强模块组（BMG）来形成降噪器。每个BMG都包含一些增强注意力融合块（BAFB）。每个BAFB由并行的空间关注单元和通道关注单元交错连接组成。此外，BAFB中的多维内部跳过连接可以承载丰富的信息功能。除了，区分器中还嵌入了空间和通道注意机制，以增强其区分各种尺寸信息的能力。同时，给出了新的损失函数以辅助模型的训练过程。BAF-GAN可以用于去除图像噪声，例如高斯噪声和混合噪声。全面的实验结果表明，BAF-GAN具有最先进的性能。