High-quality thermal infrared (IR) images are always preferred in numerous real-world applications. However, acquired IR images, which have low contrast and signal-to-noise ratio (SNR) among other characteristics, have inferior quality because of various factors. To improve the quality of IR images, three main aspects must be addressed: global contrast, local contrast, and noise of IR images. Most of the existing methods focus only on some of these issues. In this paper, we propose a novel scheme to solve the three issues. First, an edge-preserving filter called weighted least squares filter is adopted to decompose an IR image into a low-frequency (LF) component and a sequence of high-frequency (HF) components. We propose a fuzzy plateau histogram equalization for the LF component to improve global contrast. A strategy is exploited to alter the coefficients of the HF components to enhance local contrast. The primitive result is synthesized with the enhanced LF and HF components. To suppress the noise in the primitive result, nonlocal means filter is applied to derive the final result. Numerous experiments are conducted. Experimental results demonstrate that the proposed scheme exhibits the best performance compared with the other methods.
Multimedia Tools and Applications – Springer Journals
Published: Apr 8, 2017
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