In our work, an optical MEMS accelerometer based on wavelength modulation is proposed, using a double nanoscale distributed Bragg reflector (nano-DBR) structure. In view of its specific structure, this proposed accelerometer consists of a mechanical structure and an optical system, which have been studied by finite element analysis (FEA) and rigorous coupled wave analysis (RCWA) respectively. By comparing the proposed accelerometer with other previously reported works, this accelerometer has high performance in terms of sensitivity, resolution and good linearity in the whole modulation range. The effective simulation results have demonstrated as follows: a resonance frequency of 562.85 Hz, a wavelength modulation range of 1.1 to 2.63 μm, a measurement range of ±1.56 g, a mechanical sensitivity of 781.64 nm/g, an optical system sensitivity of 1.23, an accelerometer sensitivity of 964.35 nm/g, a resolution of 1.037 μg, and low cross-sensitivity. According to such functional characters, the proposed accelerometer can be used for improvements in MEMS inertial navigation devices.

Image dehazing poses significant challenges in environmental perception. Recent research mainly focus on deep learning-based methods with single modality, while they may result in severe information loss especially in dense-haze scenarios. The infrared image exhibits robustness to the haze, however, existing methods have primarily treated the infrared modality as auxiliary information, failing to fully explore its rich information in dehazing. To address this challenge, the key insight of this study is to design a visible-infrared fusion network for image dehazing. In particular, we propose a multi-scale Deep Structure Feature Extraction (DSFE) module, which incorporates the Channel-Pixel Attention Block (CPAB) to restore more spatial and marginal information within the deep structural features. Additionally, we introduce an inconsistency weighted fusion strategy to merge the two modalities by leveraging the more reliable information. To validate this, we construct a visible-infrared multimodal dataset called AirSim-VID based on the AirSim simulation platform. Extensive experiments performed on challenging real and simulated image datasets demonstrate that VIFNet can outperform many state-of-the-art competing methods. The code and dataset are available at https://github.com/mengyu212/VIFNet_dehazing.