A Unified Method for Digital Super-Resolution and Restoration in Infrared Microscopy Imaging

被引:6
|
作者
Machuca, Guillermo [1 ]
Torres, Sergio N. [1 ]
Ratliff, Bradley M. [2 ]
Gutierrez, Pablo A. [1 ]
Jara, Anselmo [1 ]
Viafora, Laura A. [1 ]
机构
[1] Univ Concepcion, Dept Ingn Elect, Casilla 106-C, Concepcion, Chile
[2] Univ Dayton, Dept Elect & Comp Engn, Dayton, OH 45459 USA
关键词
Infrared (IR) imaging; image restoration; image reconstruction; microscopy; nonuniformity correction; fixed pattern noise (FPN); resolution enhancement; super resolution; NONUNIFORMITY CORRECTION ALGORITHM; WIENER FILTER;
D O I
10.1109/ACCESS.2019.2920625
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Infrared (IR) imaging systems are known to have a range of sensor and optical limitations that result in degraded imagery. Fixed pattern noise (FPN), resulting from pixel-to-pixel response nonuniformity, is a dominant source of error that manifests in collected imagery through the appearance of temporally and spatially correlated noise patterns that are mixed with each image. Furthermore, finite detector size coupled with imperfect system optics can introduce blurring effects and aliasing, ultimately reducing resolution in acquired images. Here, we propose a unified method to reduce FPN and recover high-frequency image content in IR microscopy images. The proposed method uses regularized nonlocal means to highlight spatial features in the scene while maintaining fine textural image details. We derive an iterative optimization method based upon a gradient descent minimization strategy that applies a Wiener deconvolution in each iteration to estimate the blur artifacts. The method is implemented within an embedded mid-wave IR imaging system for microscopy applications. We demonstrate a reduction in FPN and blurring artifacts, achieving improved image resolution in the reconstructed images that are apparent in recovered details on scene objects.
引用
收藏
页码:75648 / 75657
页数:10
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