Discrete stationary wavelet transform based saliency information fusion from frequency and spatial domain in low contrast images

Due to degraded visibility and low signal-to-noise ratio properties, traditional saliency detection models face great challenges toward low contrast images. In this circumstance, it is difficult to extract effective visual features to describe saliency information. To cope with this problem, this paper proposes a salient object detection model utilizing efficient features both from frequency domain and spatial domain in low contrast images. The discrete stationary wavelet transform (DSWT) is used to fuse the saliency information from frequency and spatial domain. The input image is firstly converted into HSV color space, where each color channel is transformed into frequency domain to adjust the amplitude spectrum by a median filter. Then, a superpixel-level feature extraction is utilized to generate saliency map from both local and global spatial information. Finally, the frequency and spatial domain saliency maps are fused via DSWT to obtain the final result. Experiments are carried out on three public datasets containing visible light condition and our low contrast image dataset to demonstrate the effectiveness of the proposed saliency detection model over other ten state-of-the-art saliency models. (C) 2018 Elsevier B.V. All rights reserved.