Adaptive local histogram modification method for dynamic range compression of infrared images

  • Received : 2019.05.04
  • Accepted : 2019.06.08
  • Published : 2019.06.28


In this paper, we propose an effective dynamic range compression (DRC) method of infrared images. A histogram of infrared images has narrow dynamic range compared to visible images. Hence, it is important to apply the effective DRC algorithm for high performance of an infrared image analysis. The proposed algorithm for high dynamic range divides an infrared image into the overlapped blocks and calculates Shannon's entropy of overlapped blocks. After that, we classify each block according to the value of entropy and apply adaptive histogram modification method each overlapped block. We make an intensity mapping function through result of the adaptive histogram modification method which is using standard-deviation and maximum value of histogram of classified blocks. Lastly, in order to reduce block artifact, we apply hanning window to the overlapped blocks. In experimental result, the proposed method showed better performance of dynamic range compression compared to previous algorithms.


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Fig. 1. Histogram modification

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Fig. 2. Six types of block

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Fig. 3. overlapped block

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Fig. 4. Result of block classification (a) image (b) classification result

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Fig. 5. Histogram modification of blocks (a) Histogram modification of Fig.1.(a)(b) Histogram modification of Fig.1.(d) (c) Histogram modification of Fig.1.(f)

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Fig. 6. Result of dynamic range compression

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Fig. 8. Result image (a) Original image (b) HE (c) PHE (d) GHM (e) CLAHE (f) Proposed algorithm

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Fig. 9. Result image (a) Original image (b) HE (c) PHE (d) GHM (e) CLAHE (f) Proposed algorithm

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Fig. 10. Result image (a) CLAHE (b) Proposed algorithm

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Fig. 7. (a) Example of block artifact (b) Result of reduction block artifact

Table 1. Value of Entropy

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