Akademik Veri Yönetim Sistemi
Traitement du Signal, cilt.40, sa.1, ss.207-215, 2023 (SCI-Expanded)
In this paper, a new Signal-to-Noise Ratio (SNR) metric is proposed to quantify the detectability of targets in infrared (IR) images. The proposed metric is based on the contrast between the target and the background, which is consistent with human perception in terms of distinguishing the target from the background, rather than the raw intensity values of the target. In the contrast calculation, individual contribution of each pixel value of the target is considered in the proposed metric, whereas the mean or a single representative raw intensity value of the target is taken into account in the existing metrics. As subjective evaluations are the most precise tools for distinguishing the target from the background, SNR metrics used for IR images are expected to be as consistent as possible with the human visual system. That is, due to its high contrast sensitivity, the human visual system responds to stimuli by cognitively distinguishing the target from the background. Therefore, human perception-inspired target distinguishability metrics aim to quantify the target detectability consistent with the human visual system, which is capable of distinguishing very small differences in contrast. Extensive performance evaluation tests on well-known IR image datasets, VIVID, SENSIAC and AMCOM, and synthetic image sets demonstrate that the proposed pixel-wise SNR metric quantifies target distinguishability from the background more consistently with subjective evaluations than other SNR metrics. Furthermore, the proposed metric is always robust even when the other metrics fail to accurately quantify target distinguishability.