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SPIDEN: deep Spiking Neural Networks for efficient image denoising.

Andrea Castagnetti¹, Alain Pegatoquet¹, Benoît Miramond¹

¹Université Côte d'Azur, CNRS, LEAT, Sophia Antipolis, France.

Frontiers in Neuroscience

|August 28, 2023

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This study introduces Spiking Neural Networks (SNNs) for efficient image denoising, achieving performance close to Deep Convolutional Neural Networks (DCNNs) with reduced computational cost and energy consumption.

Area of Science:

Artificial Intelligence
Computer Vision
Computational Neuroscience

Background:

Deep Convolutional Neural Networks (DCNNs) excel at image restoration but lack computational efficiency.
Image denoising is a challenging regression task requiring high precision pixel value prediction.
Training deep networks in the spiking domain for complex tasks like denoising is difficult.

Purpose of the Study:

Investigate Spiking Neural Networks (SNNs) for image denoising.
Achieve DCNN-level performance with reduced computational cost.
Analyze the trade-off between conversion error and activation sparsity in SNNs for denoising.

Main Methods:

Formal analysis of Integrate and Fire (IF) neuron information processing.
Development of the first SNN-based image denoising solution.

Keywords:

Spiking Neural Networks denoising direct training energy consumption low latency quantization error sparsity

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Direct training of SNNs in the spike domain using surrogate gradient learning and backpropagation through time.

Main Results:

Proposed SNN achieves 30.18 dB SNR on Set12, only 0.25 dB below DCNNs.
Competitive denoising performance is achieved with low latency (few timesteps) and high sparsity.
SNNs show potential for energy efficiency, with a 20% reduction possible by increasing network size.

Conclusions:

SNNs offer a computationally efficient alternative for image denoising.
The proposed SNN training method enables high performance in the spiking domain.
SNNs present a promising direction for energy-efficient AI in image restoration tasks.