Lightweight Separable Convolutional Dehazing Network to Mobile FPGA

EasyChair Preprint 10762

Abstract

The advancement of deep learning has significantly increased the efficiency of picture dehazing techniques. Convolutional neural networks can't, however, be implemented on portable FPGA devices because to their high computing, storage, and energy needs. In this paper, we propose a generic solution for image dehazing from CNN models to mobile FPGAs. The proposed solution designs lightweight network using depth-wise separable convolution and channel attention mechanism, and uses an accelerator to increase the system's processing efficiency. We implemented the entire system on a custom and low-cost FPGA SOC platform (Xilinx Inc. ZYNQ$^{TM}$ XC7Z035). Experiments can conclude that our approach has compatible performance to GPU-based methods with much lower resource usage.

Keyphrases: Accelerator, FPGA-based dehazing, separable convolutional neural network

@booklet{EasyChair:10762,
  author    = {Xinrui Ju and Wei Wang and Xin Xu},
  title     = {Lightweight Separable Convolutional Dehazing Network to Mobile FPGA},
  howpublished = {EasyChair Preprint 10762},
  year      = {EasyChair, 2023}}