Thermal imaging plays an important role in numerous applications like in defense, security, military, and other industry that requires a non-contact temperature measurement, etc. This proposed algorithm improves the thermal image especially during the thresholding process in which the desired object is separated from the background and can easily be identified. The separate extracted images produced by K-means clustering and inverted Otsu methods were processed by Canny edge detection and color mapping to highlight the necessary characteristics of the sampled thermal images. This work is synthesized with Xilinx Zync 7000 ZED ZC702. The experimental results of this blended combination of K-means and inverted Otsu methods show significant distinguishable features in terms of edge and color. It outperforms the other color correction method in terms of processing time. Moreover, this implementation reduced resource utilization, and minimizes the misclassified pixel in different noise variance.