Uncooled infrared imaging using bimaterial microcantilever arrays

2006 ◽  
Vol 89 (7) ◽  
pp. 073118 ◽  
Author(s):  
D. Grbovic ◽  
N. V. Lavrik ◽  
P. G. Datskos ◽  
D. Forrai ◽  
E. Nelson ◽  
...  
2007 ◽  
Vol 137 (1) ◽  
pp. 13-19 ◽  
Author(s):  
Zheying Guo ◽  
Qingchuan Zhang ◽  
Fengliang Dong ◽  
Dapeng Chen ◽  
Zhiming Xiong ◽  
...  

2006 ◽  
Author(s):  
N. V. Lavrik ◽  
D. Grbovic ◽  
S. Rajic ◽  
P. G. Datskos ◽  
D. Forrai ◽  
...  

2013 ◽  
Vol 427-429 ◽  
pp. 1948-1951
Author(s):  
Jia Lin Ma ◽  
Xia Zhang

Uncooled infrared imaging system has been increasingly applied in both the national defense and various fields of national economy. Such popularity is attributed to many of its advantages, including small size, light weight, low energy-consumption and superior portability. However, as limited by the structure and the material of infrared detector and the manufacturing techniques, infrared images are plagued with low resolution and poor image quality. This paper mainly studies the uncooled infrared image processing based on the gray levels partition processing, gray levels stretching and histogram modification, it aims to enhance the visual effect of infrared image.


2019 ◽  
Vol 9 (10) ◽  
pp. 1993 ◽  
Author(s):  
Ende Wang ◽  
Ping Jiang ◽  
Xukui Hou ◽  
Yalong Zhu ◽  
Liangyu Peng

In the uncooled infrared imaging systems, owing to the non-uniformity of the amplifier in the readout circuit, the infrared image has obvious stripe noise, which greatly affects its quality. In this study, the generation mechanism of stripe noise is analyzed, and a new stripe correction algorithm based on wavelet analysis and gradient equalization is proposed, according to the single-direction distribution of the fixed image noise of infrared focal plane array. The raw infrared image is transformed by a wavelet transform, and the cumulative histogram of the vertical component is convolved by a Gaussian operator with a one-dimensional matrix, in order to achieve gradient equalization in the horizontal direction. In addition, the stripe noise is further separated from the edge texture by a guided filter. The algorithm is verified by simulating noised image and real infrared image, and the comparison experiment and qualitative and quantitative analysis with the current advanced algorithm show that the correction result of the algorithm in this paper is not only mild in visual effect, but also that the structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) indexes can get the best result. It is shown that this algorithm can effectively remove stripe noise without losing details, and the correction performance of this method is better than the most advanced method.


2008 ◽  
Vol 108 (6) ◽  
pp. 579-588 ◽  
Author(s):  
Fengliang Dong ◽  
Qingchuan Zhang ◽  
Dapeng Chen ◽  
Zhengyu Miao ◽  
Zhiming Xiong ◽  
...  

2007 ◽  
Vol 3 (2) ◽  
pp. 119-122 ◽  
Author(s):  
Q. Zhang ◽  
Z. Miao ◽  
Z. Guo ◽  
F. Dong ◽  
Z. Xiong ◽  
...  

2003 ◽  
Author(s):  
James W. Hoffman ◽  
Philip J. Riggan ◽  
Stephanie A. Griffin ◽  
Ronald C. Grush ◽  
William H. Grush ◽  
...  

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