LAUSR

During hazy or foggy conditions, the acquired images are degraded and resulting in reduced visibility, contrast and color fidelity. This image degradation occurs due to atmospheric particles that attenuates and scatters the source radiations. The degradation intensity depends on diverse scenarios having variable densities of atmospheric particles, their wavelength and distance from acquisition device. Existing image dehazing methods for visible-band images are either based on prior assumptions to reconstruct the transmission map or used some learning mechanism to directly estimate the dehazed image. Recently, performance comparison of existing popular image dehazing methods using spectral hazy images are performed in which selected wavelength bands from different fog density levels are used for comparisons. The comparison results showed performance degradation of existing methods with wavelength bands selection and fog density levels. In this study, we design an effective spectral and prior based image dehazing and enhancement network (SPIDE-Net) showing better performance as compared to existing methods when using spectral hazy images from variable wavelength bands and fog density levels. Our SPIDE-Net consists of two networks:1) Spectral Image Dehazing Network (SID-Net), which is trained on multi-spectral hazy images between 450 nm and 720 nm, and takes advantage of varying attenuations in different wavelength bands. 2) Multi-scale Prior based image Dehazing Network (MPD-Net) uses multi-scale dark-channel and color attenuation priors on image triplets selected from a multi-spectral hazy image database. The proposed method is an encoder-decoder style CNN network that combines information from both SID-Net and MPD-Net by sharing a common decoder stage. The proposed network was trained on the SHIA dataset and evaluated at different fog density levels. Compared with popular prior and learning-based methods evaluated at SHIA dataset, the proposed method achieves superior performance both qualitatively and quantitatively.