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http://dx.doi.org/10.5573/ieie.2016.53.11.073

Infrared Image Sharpness Enhancement Method Using Super-resolution Based on Adaptive Dynamic Range Coding and Fusion with Visible Image  

Kim, Yong Jun (Department of Electronic Engineering, Inha University)
Song, Byung Cheol (Department of Electronic Engineering, Inha University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.53, no.11, 2016 , pp. 73-81 More about this Journal
Abstract
In general, infrared images have less sharpness and image details than visible images. So, the prior image upscaling methods are not effective in the infrared images. In order to solve this problem, this paper proposes an algorithm which initially up-scales an input infrared (IR) image by using adaptive dynamic range encoding (ADRC)-based super-resolution (SR) method, and then fuses the result with the corresponding visible images. The proposed algorithm consists of a up-scaling phase and a fusion phase. First, an input IR image is up-scaled by the proposed ADRC-based SR algorithm. In the dictionary learning stage of this up-scaling phase, so-called 'pre-emphasis' processing is applied to training-purpose high-resolution images, hence better sharpness is achieved. In the following fusion phase, high-frequency information is extracted from the visible image corresponding to the IR image, and it is adaptively weighted according to the complexity of the IR image. Finally, a up-scaled IR image is obtained by adding the processed high-frequency information to the up-scaled IR image. The experimental results show than the proposed algorithm provides better results than the state-of-the-art SR, i.e., anchored neighborhood regression (A+) algorithm. For example, in terms of just noticeable blur (JNB), the proposed algorithm shows higher value by 0.2184 than the A+. Also, the proposed algorithm outperforms the previous works even in terms of subjective visual quality.
Keywords
Infrared image; edge enhancement; image fusion;
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