Nonuniformity Correction Algorithm Based on the Infrared Focal Plane Array Readout Architecture

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Nonuniformity Correction Algorithm Based on the Infrared Focal Plane Array Readout Architecture

Article | 更新时间：2020-08-12

- Nonuniformity Correction Algorithm Based on the Infrared Focal Plane Array Readout Architecture
- Optics and Precision Engineering Vol. 16, Issue 1, Pages: 128-133(2008)
- 作者机构：
  
  西安电子科技大学模式识别与智能控制研究所
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Received：01 August 2007，
  
  Revised：23 August 2007，
  
  Published Online：22 January 2008，
  
  Published：22 January 2008
- 稿件说明：
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刘永进,朱红,赵亦工. 基于红外焦平面阵列读出结构的非均匀校正算法[J]. 光学精密工程, 2008,16(1):128-133

Nonuniformity Correction Algorithm Based on the Infrared Focal Plane Array Readout Architecture[J]. Optics and precision engineering, 2008, 16(1): 128-133.
刘永进,朱红,赵亦工. 基于红外焦平面阵列读出结构的非均匀校正算法[J]. 光学精密工程, 2008,16(1):128-133 DOI：

Nonuniformity Correction Algorithm Based on the Infrared Focal Plane Array Readout Architecture[J]. Optics and precision engineering, 2008, 16(1): 128-133. DOI：

摘要

空间固定图案噪声严重影响了红外探测器的成像质量，它主要是由于焦平面阵列上各探测单元对于同一辐照的光电响应不完全一致所造成。另外，多通道图像数据采集电路的非均匀性更加重了这种噪声。本文提出了一种基于红外焦平面阵列读出结构的非均匀校正算法，它将校正过程分成两步。首先利用恒定统计方法滤除由通道放大器引起的固定图案噪声，然后再将处理结果通过改进的神经网络方法，进一步校正由探测器响应而产生的非均匀性。由于该算法是递推进行，并且具有收敛速度快、计算复杂度低等优点，因此易于工程实现。文中详细阐述了算法的基本原理及其流程，并用仿真数据和真实红外数据对算法性能进行验证，取得了较好的效果。

Abstract

The performance of an infrared imaging system is affected by spatial fixed-pattern noise

which is mainly due to the different photoresponse of each detector in a focal plane array for the same irradiance. In addition

the nonuniformity of the readout

which is involved in multichannel image data processing

causes the further noise. A novel nonuniformity correction algorithm based on the infrared focal plane array readout architecture is developed

which corrects the aggregate nonuniformity by two separate steps. First

the nonuniformity from the readout amplifiers is corrected by using the constant statistics method. Second

the noise resulting from the detectors is eliminated by exploiting the improved neural network method. The strength of the proposed algorithm lies in its high convergent speed and low computational complexity

and the performance is demonstrated with both simulated and real infrared imagery.

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Related Institution

Ningbo Research Institute， Dalian University of Technology

Liaoning Provincial Key Laboratory of Micro Nanosystems， Dalian University of Technology

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Wuxi Shangshi-finevision Technology Co.， Ltd

College of Computer Science and Technology， Jilin University

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