ECANet: enhanced context aggregation network for single image dehazing

Image dehazing is an important problem since computer recognition requires high-quality inputs. Recently, many researches tend to build an end-to-end multiscale network to restore haze-free images. But unfortunately, existing multiscale networks tend to recover under-dehazed results due to inefficient feature extraction. To solve the problem, we propose an enhanced context aggregation network for single image dehazing named ECANet. Based on encoder-decoder structure, the ECANet improves feature representation by three feature aggregation blocks (FABs) on each scale. The FAB is a new efficient feature extraction module, which adequately extracts content features and style features due to the difference receptive field between dilated convolution and ordinary convolution. To better fuse these complementary features, we combine spatial and channel attention mechanism to each FAB. After the decoding process, we also adopt an enhancing block to further refine image details under the supervision of clear references. The experimental results show that the proposed ECANet performs better than state-of-the-art dehazing methods, which recovers clear images with discriminative texture and natural color.