Resolution Improvement of Infrared Images Using Visible Image Information

Choi, Kyuha; Kim, Changhyun; Kang, Myung-Ho; Beom, Jong

doi:10.1109/lsp.2011.2165842

Cited by 15 publications

(7 citation statements)

References 8 publications

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“…On the other hand, thermal cameras work with the heat radiation emitted by objects so they do not require visible illumination to function; instead daylight is often avoided when thermal imaging is used for diagnostic purposes. Among the drawbacks inherent to thermal images are low spatial resolution, poor quality and lack of visual detail (Choi et al 2011;Morris et al 2007;Prakash 2000). The respective pros and cons of thermal and visible images make them complement each other very well so both modalities are widely employed in building diagnostics (Balaras and Argiriou 2002;Gade and Moeslund 2014;Kylili et al 2014) and surveillance systems for human detection and tracking (Gade and Moeslund 2014;Kong et al 2007;Kumar et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Infrared-visible image registration for augmented reality-based thermographic building diagnostics

Liu

Seipel

2015

Vis. in Eng.

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Background: In virtue of their capability to measure temperature, thermal infrared cameras have been widely used in building diagnostics for detecting heat loss, air leakage, water damage etc. However, the lack of visual details in thermal infrared images makes the complement of visible images a necessity. Therefore, it is often useful to register images of these two modalities for further inspection of architectures. Augmented reality (AR) technology, which supplements the real world with virtual objects, offers an ideal tool for presenting the combined results of thermal infrared and visible images. This paper addresses the problem of registering thermal infrared and visible façade images, which is essential towards developing an AR-based building diagnostics application. Methods: A novel quadrilateral feature is devised for this task, which models the shapes of commonly present façade elements, such as windows. The features result from grouping edge line segments with the help of image perspective information, namely, vanishing points. Our method adopts a forward selection algorithm to determine feature correspondences needed for estimating the transformation model. During the formation of the feature correspondence set, the correctness of selected feature correspondences at each step is verified by the quality of the resulting registration, which is based on the ratio of areas between the transformed features and the reference features. Results and conclusions:Quantitative evaluation of our method shows that registration errors are lower than errors reported in similar studies and registration performance is usable for most tasks in thermographic inspection of building façades.

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Section: Introductionmentioning

confidence: 99%

Infrared-visible image registration for augmented reality-based thermographic building diagnostics

Liu

Seipel

2015

Vis. in Eng.

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“…- [422], [480], [481], [482], [515], [537], [544], [547] where a reconstruction based SR is followed by a learning based SR.…”

Section: Combined Methodsmentioning

confidence: 99%

Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

588

292

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Super-resolution, the process of obtaining one or more high-resolution images from one or more low-resolution observations, has been a very attractive research topic over the last two decades. It has found practical applications in many real world problems in different fields, from satellite and aerial imaging to medical image processing, to facial image analysis, text image analysis, sign and number plates reading, and biometrics recognition, to name a few. This has resulted in many research papers, each developing a new superresolution algorithm for a specific purpose. The current comprehensive survey provides an overview of most of these published works by grouping them in a broad taxonomy. For each of the groups in the taxonomy, the basic concepts of the algorithms are first explained and then the paths through which each of these groups have evolved are given in detail, by mentioning the contributions of different authors to the basic concepts of each group. Furthermore, common issues in super-resolution algorithms, such as imaging models and registration algorithms, optimization of the cost functions employed, dealing with color information, improvement factors, assessment of super-resolution algorithms, and the most commonly employed databases are discussed.

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“…These operators have poor performance to the infrared image. To resolve this problem, the phase congruency method [18][19][20][21][22] is adopted to detect infrared image edges and visible image edges to generate infrared edge maps and visible edge maps respectively.…”

Section: A Phase Congruencymentioning

confidence: 99%

“…The phase congruency is an image feature perception model, which postulates that features are perceived at points in an image where Fourier components are maximal in phase [20]. The phase congruency function of a signal I at a location x can be defined in terms of the Fourier series expansion [21]:…”

Section: A Phase Congruencymentioning

confidence: 99%

An Improving Infrared Image Resolution Method via Guided Image Filtering

Gan¹

2014

JSW

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Image resolution is of importance to image processing. In super resolution methods, images obtained from same sensor are adopted to improve the resolution of image. However these methods do not take advantage of the correlation of the images from different sensors to improve the resolution of image. To solve this problem, in this paper a method is proposed to enhance infrared image by using the correlation of an infrared (IR) image and its corresponding visible image. Firstly, phase congruency is used to generate the edge maps of the infrared and visible images. Then, correlated edge regions and uncorrelated regions are calculated according to the edge maps. Finally, different strategies are applied to those regions. Specifically, for the correlated regions, the visible image is considered as the guidance image while for uncorrelated regions, the infrared image itself is considered as the guidance image. Hereafter guided image filter is applied to the infrared image. Finally, the filtered result of correlated edge regions and uncorrelated regions are combined to obtain the final result. The resultant image inferred by the proposed method is with better subjective and objective quality compared with other methods.

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Resolution Improvement of Infrared Images Using Visible Image Information

Cited by 15 publications

References 8 publications

Infrared-visible image registration for augmented reality-based thermographic building diagnostics

Infrared-visible image registration for augmented reality-based thermographic building diagnostics

Super-resolution: a comprehensive survey

An Improving Infrared Image Resolution Method via Guided Image Filtering

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