ReBo-SNN: Boosted spiking neural network with retina mechanism for efficient haze removal.

Abstract

The haze effect induces light attenuation and color shifting in distant objects, thereby diminishing the contrast and saturation of images. In pursuit of restoring image clarity, a plethora of dehazing methods have been successively introduced. Nevertheless, the development of effective and practical methodologies remains a formidable challenge due to the requirement for powerful feature extraction capability alongside consideration of computational efficiency and model scalability. To address these issues, we put forward a boosted spiking neural network with retina mechanism, termed ReBo-SNN, for the reconstruction of hazy images. Specifically, the proposed network draws inspiration from the antagonistic mechanisms of retinal neurons and innovatively devises a retinal module. By extracting highly expressive features through ON and OFF roads, the retinal module can effectively augment visual resolution and contrast. Furthermore, to circumvent the issue of excessive smoothing, the network introduces a novel boosting strategy that realizes progressive feature enhancement and assists in reconstructing high-quality dehazed images. In addition, the network also incorporates dense residual connections with strided convolution to compensate for potential information loss during the translation, which significantly enhances the efficiency of feature transmission and integration. Extensive experiments on both synthetic and real-world hazy images demonstrate that our proposed network achieves superior quantitative and qualitative results. Based on the spiking mechanism and compact architecture, our network also exhibits a substantial energy efficiency advantage, which promotes its deployment in vision hardware to enhance overall performance.