Tone mapping infrared images using conditional filtering-based multi-scale retinex

Haibo Luo; Lingyun Xu; Bin Hui; Zheng Chang

doi:10.1117/12.2202187

8 October 2015 Tone mapping infrared images using conditional filtering-based multi-scale retinex

Haibo Luo, Lingyun Xu, Bin Hui, Zheng Chang

Proceedings Volume 9675, AOPC 2015: Image Processing and Analysis; 96752D (2015) https://doi.org/10.1117/12.2202187
Event: Applied Optics and Photonics China (AOPC2015), 2015, Beijing, China

Abstract

Tone mapping can be used to compress the dynamic range of the image data such that it can be fitted within the range of the reproduction media and human vision. The original infrared images that captured with infrared focal plane arrays (IFPA) are high dynamic images, so tone mapping infrared images is an important component in the infrared imaging systems, and it has become an active topic in recent years. In this paper, we present a tone mapping framework using multi-scale retinex. Firstly, a Conditional Gaussian Filter (CGF) was designed to suppress "halo" effect. Secondly, original infrared image is decomposed into a set of images that represent the mean of the image at different spatial resolutions by applying CGF of different scale. And then, a set of images that represent the multi-scale details of original image is produced by dividing the original image pointwise by the decomposed image. Thirdly, the final detail image is reconstructed by weighted sum of the multi-scale detail images together. Finally, histogram scaling and clipping is adopted to remove outliers and scale the detail image, 0.1% of the pixels are clipped at both extremities of the histogram. Experimental results show that the proposed algorithm efficiently increases the local contrast while preventing “halo” effect and provides a good rendition of visual effect.

Citation Download Citation

Haibo Luo, Lingyun Xu, Bin Hui, and Zheng Chang "Tone mapping infrared images using conditional filtering-based multi-scale retinex", Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 96752D (8 October 2015); https://doi.org/10.1117/12.2202187

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