Spiking transformer with learnable threshold mechanism for underwater image dehazing to aid vision-based navigation

Abstract In this paper, we introduce snnTrans-DHZ, a lightweight spiking transformer architecture that incorporates a learnable threshold membrane potential mechanism for underwater image dehazing. Despite its compact design with 0.57 M parameters, the method substantially improves underwater image clarity and visibility. Leveraging the temporal dynamics of spiking neural networks, snnTrans-DHZ efficiently processes time-dependent raw image sequences while maintaining low power consumption. The raw underwater images are first converted into time-dependent image sequences by repeatedly passing the static image to a user-defined timestep value. The RGB sequences are then converted into LAB color space representations and processed simultaneously. The architecture integrates three primary modules: (i) K estimator module to extract features from different color space representations, (ii) background light estimator module to jointly estimate the background light component from the RGB-LAB color space representations, and (iii) soft image reconstruction to reconstruct the haze-free, visibility-enhanced image. The snnTrans-DHZ model is directly trained using surrogate gradient-based backpropagation through time strategy. In this research, a combined loss function is designed and used. Our model is trained and tested on the UIEB and EUVP, the two publicly available benchmark dataset for image dehazing. Our algorithm achieves a PSNR of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mn>21.6773</mml:mn> <mml:mstyle scriptlevel="0"/> <mml:mtext>dB</mml:mtext> </mml:mrow> </mml:math> and SSIM of 0.8795 on UIEB and, on EUVP, it achieves <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mn>23.4562</mml:mn> <mml:mstyle scriptlevel="0"/> <mml:mtext>dB</mml:mtext> </mml:mrow> </mml:math> and 0.8439. snnTrans-DHZ algorithm achieves this algorithmic performance with fewer operations (7.42 GSOPs) and lower energy consumption of 0.0151 J compared to existing state-of-the-art image enhancement methods. It provides a 3.3 × improvement in energy efficiency over the lightest state-of-the-art transformer-based method, making it suitable for underwater robotics and environmental monitoring. The source code is available at snnTrans-DHZ .