Fan Guodong, Gan Min, Fan Bi, Chen C L Philip
IEEE Trans Neural Netw Learn Syst. 2024 Feb;35(2):1598-1612. doi: 10.1109/TNNLS.2022.3184164. Epub 2024 Feb 5.
In this article, we propose a multiscale cross-connected dehazing network with scene depth fusion. We focus on the correlation between a hazy image and the corresponding depth image. The model encodes and decodes the hazy image and the depth image separately and includes cross connections at the decoding end to directly generate a clean image in an end-to-end manner. Specifically, we first construct an input pyramid to obtain the receptive fields of the depth image and the hazy image at multiple levels. Then, we add the features of the corresponding dimensions in the input pyramid to the encoder. Finally, the two paths of the decoder are cross-connected. In addition, the proposed model uses wavelet pooling and residual channel attention modules (RCAMs) as components. A series of ablation experiments shows that the wavelet pooling and RCAMs effectively improve the performance of the model. We conducted extensive experiments on multiple dehazing datasets, and the results show that the model is superior to other advanced methods in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and subjective visual effects. The source code and supplementary are available at https://github.com/CCECfgd/MSCDN-master.
在本文中，我们提出了一种具有场景深度融合的多尺度交叉连接去雾网络。我们关注模糊图像与相应深度图像之间的相关性。该模型分别对模糊图像和深度图像进行编码和解码，并在解码端包含交叉连接，以端到端的方式直接生成清晰图像。具体来说，我们首先构建一个输入金字塔，以获得多个层次上深度图像和模糊图像的感受野。然后，我们将输入金字塔中相应维度的特征添加到编码器中。最后，解码器的两条路径进行交叉连接。此外，所提出的模型使用小波池化和残差通道注意力模块（RCAM）作为组件。一系列消融实验表明，小波池化和RCAM有效地提高了模型的性能。我们在多个去雾数据集上进行了广泛的实验，结果表明该模型在峰值信噪比（PSNR）、结构相似性（SSIM）和主观视觉效果方面优于其他先进方法。源代码和补充内容可在https://github.com/CCECfgd/MSCDN-master获取。
