Infrared and Visible Image Fusion Using Entropy and Neuro-Fuzzy Concepts
Image fusion is the process to derive the useful information from the scene captured by infrared (IR) and visible images. This derived information is used to improve the image content by enhancing the image visualization. Human identification or any living object identification in IR images is a challenging task. This paper proposes two fusion techniques namely Discrete Wavelet Transform with Neuro-Fuzzy (NF) and Entropy (EN) (DWT-NF-EN) and Integer Wavelet Transform with Neuro-Fuzzy and Entropy (IWT-NF-EN) and their results are compared and analyzed with existing fusion techniques using different quantitative measures. Subjective and objective evaluation of the results obtained is compared with other fusion techniques namely Redundancy Discrete Wavelet Transform (RDWT) and Integer Wavelet Transform and Neuro-Fuzzy (IWT-NF). The objective evaluation is done using the quantitative measures Entropy (EN), Peak Signal to Noise Ratio (PSNR) and Normalized Correlation Coefficient (NCC). From the experimental results it is observed that proposed methods provided better information (quality) using EN, PSNR and NCC measures for majority of the test images and the same is justified with the subjective results.
KeywordsInfrared and visible images Integer Wavelet Transform Discrete Wavelet Transform Neuro-Fuzzy RDWT Fusion
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