Spatial frequency enhanced salient object detection

Many salient object detection (SOD) methods based on convolutional neural networks utilize spatial frequency information from images to obtain salient features. However, most of these methods do not extract and/or exploit spatial frequency information effectively. This results in suboptimal detection performance in complex natural and human social scenes. In this work, we first propose an effective and flexible spatial frequency enhancement (SFE) module based on generalized Oct-convolution. SFE can effectively extract and incorporate multiple spatial frequency information from in-stage and cross-stage feature maps and output an arbitrary number of comprehensive and compact frequency features. Then we design a spatial frequency enhanced network (SFENet) adopting two SFE modules to refine high- and low-frequency salient features end-to-end. High- and low-frequency salient features are then integrated into a final full-band saliency prediction. Experiments show that SFENet can ensure the integrity and sharpness of salient regions in complex backgrounds on five benchmark datasets.