MixDehazeNet : Mix Structure Block For Image Dehazing Network
LiPing Lu Qian Xiong DuanFeng Chu BingRong Xu
Image dehazing is a typical task in the low-level vision field. Previous
studies verified the effectiveness of the large convolutional kernel and
attention mechanism in dehazing. However, there are two drawbacks: the
multi-scale properties of an image are readily ignored when a large
convolutional kernel is introduced, and the standard series connection of an
attention module does not sufficiently consider an uneven hazy distribution. In
this paper, we propose a novel framework named Mix Structure Image Dehazing
Network (MixDehazeNet), which solves two issues mentioned above. Specifically,
it mainly consists of two parts: the multi-scale parallel large convolution
kernel module and the enhanced parallel attention module. Compared with a
single large kernel, parallel large kernels with multi-scale are more capable
of taking partial texture into account during the dehazing phase. In addition,
an enhanced parallel attention module is developed, in which parallel
connections of attention perform better at dehazing uneven hazy distribution.
Extensive experiments on three benchmarks demonstrate the effectiveness of our
proposed methods. For example, compared with the previous state-of-the-art
methods, MixDehazeNet achieves a significant improvement (42.62dB PSNR) on the
SOTS indoor dataset. The code is released in
https://github.com/AmeryXiong/MixDehazeNet.