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BGIR: A Low-Illumination Remote Sensing Image Restoration Algorithm with ZYNQ-Based Implementation.

Zhihao Guo1,2,3, Liangliang Zheng1,2,3, Wei Xu1,2,3

  • 1Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.

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Summary
This summary is machine-generated.

This study introduces the Bilateral-Guided Image Restoration (BGIR) algorithm to enhance dark remote sensing images from Complementary Metal-Oxide-Semiconductor (CMOS) systems. The BGIR algorithm significantly boosts image quality and processing speed for real-time applications.

Keywords:
HSV spaceRetinex algorithmZYNQimage restoration systemremote sensing images

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Area of Science:

  • Remote Sensing
  • Image Processing
  • Computer Vision

Background:

  • Complementary Metal-Oxide-Semiconductor (CMOS) imaging systems face limitations in low-light conditions at high frame rates, resulting in dark images with poor signal-to-noise ratio and sharpness.
  • Effective enhancement of remote sensing images is crucial for accurate data interpretation and analysis.

Purpose of the Study:

  • To propose a novel low-light remote sensing image enhancement method and a Zynq (Zynq-7000 All Programmable SoC) hardware design scheme.
  • To improve the visibility, signal-to-noise ratio, and sharpness of images acquired by CMOS imaging systems operating under challenging lighting conditions.

Main Methods:

  • An improved multi-scale Retinex algorithm in the hue-saturation-value (HSV) color space was developed.
  • The method involves separating RGB into H, S, and V components, processing the V component with bilateral filtering, applying gamma correction, and enhancing the S component.
  • The algorithm was deployed on a Zynq platform using ARM + FPGA synergy, incorporating lookup tables and pipelining for acceleration.

Main Results:

  • The Bilateral-Guided Image Restoration (BGIR) algorithm demonstrated a processing speed improvement of nearly 30 times compared to existing methods while preserving image quality.
  • End-to-end deployment achieved processing speeds of 80 fps for 640 × 480 resolution and 30 fps for 1280 × 720 resolution.
  • The solution offers advantages of fast processing, miniaturization, embeddability, and portability.

Conclusions:

  • The BGIR algorithm effectively enhances low-light remote sensing images acquired by CMOS systems.
  • The Zynq hardware implementation satisfies the performance requirements for real-time, high-speed imaging applications.
  • The proposed method provides a practical and efficient solution for improving remote sensing image visibility and quality.