School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea.
Sensors (Basel). 2023 Mar 11;23(6):3033. doi: 10.3390/s23063033.
As the demand for thermal information increases in industrial fields, numerous studies have focused on enhancing the quality of infrared images. Previous studies have attempted to independently overcome one of the two main degradations of infrared images, fixed pattern noise (FPN) and blurring artifacts, neglecting the other problems, to reduce the complexity of the problems. However, this is infeasible for real-world infrared images, where two degradations coexist and influence each other. Herein, we propose an infrared image deconvolution algorithm that jointly considers FPN and blurring artifacts in a single framework. First, an infrared linear degradation model that incorporates a series of degradations of the thermal information acquisition system is derived. Subsequently, based on the investigation of the visual characteristics of the column FPN, a strategy to precisely estimate FPN components is developed, even in the presence of random noise. Finally, a non-blind image deconvolution scheme is proposed by analyzing the distinctive gradient statistics of infrared images compared with those of visible-band images. The superiority of the proposed algorithm is experimentally verified by removing both artifacts. Based on the results, the derived infrared image deconvolution framework successfully reflects a real infrared imaging system.
随着工业领域对热信息的需求增加，大量研究致力于提高红外图像的质量。之前的研究试图独立克服红外图像的两种主要退化之一，即固定模式噪声（FPN）和模糊伪影，而忽略另一个问题，以降低问题的复杂性。然而，对于现实世界中的红外图像来说，这是不可行的，因为两种退化同时存在并相互影响。在这里，我们提出了一种联合考虑单一框架中 FPN 和模糊伪影的红外图像反卷积算法。首先，推导出了一个包含热信息采集系统的一系列退化的红外线性退化模型。随后，基于对列 FPN 视觉特征的研究，开发了一种能够精确估计 FPN 分量的策略，即使存在随机噪声也是如此。最后，通过分析与可见光波段图像相比红外图像的独特梯度统计信息，提出了一种非盲图像反卷积方案。实验验证了该算法通过去除两种伪影的优越性。基于结果，所推导的红外图像反卷积框架成功地反映了真实的红外成像系统。
