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Found 6 papers, 1 papers with code
snnTrans-DHZ: A Lightweight Spiking Neural Network Architecture for Underwater Image Dehazing
Underwater image dehazing is critical for vision-based marine operations because light scattering and absorption can severely reduce visibility.
Underwater Image Enhancement by Convolutional Spiking Neural Networks
Our algorithm performs on par with its non-spiking counterpart methods in terms of PSNR and structural similarity index (SSIM) at reduced timesteps ($T=5$) and energy consumption of $85\%$.
Dynamic Spiking Framework for Graph Neural Networks
Current work often uses SNNs instead of Recurrent Neural Networks (RNNs) by using binary features instead of continuous ones for efficient training, which would overlooks graph structure information and leads to the loss of details during propagation.
Energy Efficient Training of SNN using Local Zeroth Order Method
To circumvent the problem surrogate method uses a differentiable approximation of the Heaviside in the backward pass, while the forward pass uses the Heaviside as the spiking function.
Underwater Image Enhancement Using Pre-trained Transformer
The goal of this work is to apply a denoising image transformer to remove the distortion from underwater images and compare it with other similar approaches.
Underwater Object Classification and Detection: first results and open challenges
First, we assessed if pretraining with the conventional ImageNet is beneficial when the object detector needs to be applied to environments that may be characterised by a different feature distribution.
