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Local contrast measure (LCM) has been proved to be an effective method for infrared small target detection. However, the detection performance of LCM decreases dramatically when the background contains strong edges and pixel‐sized noises with high brightness (PNHB). Based on the analysis of the inherent causes of the poor performance of LCM in extremely complex backgrounds, this study presents an effective LCM with an iterative error. The contribution is as follows: first, the two‐dimensional least mean square (TDLMS) filter with an adaptive parameter is applied to suppress the background clutters roughly in each multiscale window. Then, the partial maximum pixel mean is applied to the LCM to optimise the sub‐block statistical parameters, which achieves excellent strong edges suppression performance. Finally, the iteration error generated by TDLMS and the sub‐block weight matrix is updated alternately to further optimise the statistical parameters of the contrast measure to make it more effective in suppressing PNHB. Experimental results demonstrate that the proposed approach is not only superior to the contrast methods in terms of high detection efficiency and low false alarm rate but also has satisfactory adaptability under extremely complex backgrounds. [ABSTRACT FROM AUTHOR]

Contrast enhancement of an image can be performed by using a simple histogram equalisation (HE) technique. However, there are some drawbacks of HE like immense brightness change, artificial effects, over‐enhancement, which make it unsuitable to be used in many applications. To resolve these issues a new adaptive heuristic HE approach is proposed in this study. First, probability distribution function (PDF) of the image is calculated. Second, an adaptive parameter is calculated based on the mean and maximum values of that PDF. Thereafter, PDF and cumulative distribution function (CDF) are modified by applying a threshold limit to that adaptive parameter. Finally, another adaptive parameter is finding out by using modified CDF and a new CDF is obtained by using this second adaptive parameter. Traditional HE is then applied with the new CDF to getting the enhanced image. The visual and quantitative results of the proposed method outperform all other state‐of‐the‐art papers and works well both for low and bright contrast images simultaneously. After rigorous experiment, it is concluded that the authors' method enhances the image contrast very well with no over‐enhancement or artificial effects in the images and also preserves the original characteristics of the input images. [ABSTRACT FROM AUTHOR]