Saliency Computational Model for Foggy Images by Fusing Frequency and Spatial Cues

Abstract

A key challenge of saliency computation in foggy images is how to effectively detect salient objects which are less visible. The primary cause may lie in the fact that the light scattering through fog particles reduces image contrast. In this paper, we propose a frequency-spatial fusion saliency computational model based on discrete stationary wavelet transform (DSWT). The input image is firstly transformed into HSV color space, and the amplitude spectrum of each color channel is adjusted to generate the frequency domain saliency map. Then, the local-global superpixel contrast is measured to obtain the spatial domain saliency map. The DSWT is finally utilized to fuse the frequency-spatial cues. Experimental results indicate that the proposed model can efficiently reduce the influence of light scattering through fog particles, and can achieve the best performance in foggy images comparing to 16 state-of-the-art saliency models.