SCPA‐Net: Self‐calibrated pyramid aggregation for image dehazing
Zhihua Chen, Yu Zhou, Ran Li, Ping Li, Bin Sheng
Abstract
Abstract Dehazing as an important image processing field has developed for many years, there exist many excellent methods for exploring more complex networks to solve this problem. In this paper, instead of designing a complex network structure, we propose a novel dehazing network based on the consideration of enhancing feature aggregation and feature representation abilities of dehazing architecture. Specifically, we propose a self‐calibrated pyramid aggregation network (SCPA‐Net) for image dehazing, which is based on an encoder‐decoder architecture. In the encoder, we build a self‐attention block as a unit to aggregate information from a neighborhood to adapt to its content. In the decoder, we introduce a self‐calibration block to capture long‐range spatial and channel dependencies to produce more discriminative representations. Finally, to learn the scale information, the pyramid upsampling structure is applied to aggregate the multiscale self‐calibrated attentive features. Experimental results show our SCPA‐Net can achieve impressive dehazing performance.