RESUMO
Infrared (IR) images are basically low-contrast in nature; hence, it is essential to enhance the contrast of IR images to facilitate real-life applications. This work proposes a novel adaptive clip-limit-oriented bi-histogram equalization (bi-HE) method for enhancing IR images. HE methods are simple in implementation but often cause over-enhancement due to the presence of long spikes. To reduce long spikes, this work suggests to apply a log-power operation on the histogram, where the log operation reduces the long spikes, and power transformation regains the shape of the histogram. First, a histogram separation point is generated applying the mean of the multi-peaks of the input histogram. After that, an alteration in the input histogram is done using the log-power process. Subsequently, a clipping operation on the altered histogram followed by redistribution of the clipped portion is performed to restrict over-enhancement. Next, the modified histogram is sub-divided using the histogram separation point. Finally, the modified sub-histograms are equalized independently. Simulation results show that the suggested method effectively improves the contrast of IR images. Visual quality evaluations and quantitative assessment demonstrate that the suggested method outperforms the state-of-the-art algorithms.
RESUMO
The effect of induced fear in the prey due to the presence of a predator can alone develop anti-predator resistance to such an extent that it might reduce the prey reproduction in a significant amount. As fear can perceptibly affect the densities of the terrestrial vertebrates, here we investigate the cost of fear on a Holling type II predator-prey model associated with prey refuge and additional food to the predator. We evidently provide conditions on the existence and stability of equilibria as well as the occurrence of the Hopf bifurcation. Besides, we study how the presence of fear re-calibrates the effects of prey refuge and additional food in terms of quality and quantity on the system dynamics. We observe that the effect of fear not only reduces the predator density but also considerably drives the system toward stability. All the results presented here are mainly analytical but to make it more comprehensive, we perform numerical simulation. Moreover, to show the vast applicability of our results, we compare it with a few other models according to the presence of fear, refuge, and additional food.