An Adaptive Tuning Stochastic Resonance Approach for Image Enhancement on Illumination Variant Images
Noriko Kojima, Bikash Lamsal, Naofumi Matsumoto · Journal of the Institute of Industrial Applications Engineers · 2017
This paper is associated with an adaptive adaptive tuning stochastic resonance approach for image enhancement on illumination variant images. This new process is developed being based on our previous works related to image enhancement by using stochastic resonance by using the manual tuning process. The process was performed by adding randomly the noise and threshold in an image. The random addition of the noise and threshold lacks the effectivity and is time consuming. The current process is the developed system of our previous works with the introduction of adaptive tuning. The process works properly in the dark and very low contrast images as well as bright images based and mixed illumination variant images. This system works on the images with the mixture of darkness and brightness. The SR is applied by combining the logical AND with the stochastic resonance. We also present the idea of adaptive tuning of the summation iteration with random noise and threshold value by using the process related to the histogram calculation or mean median and mode. The combination of the logical AND with the SR and the idea of adaptive tuning of SR reflects the novelty of our paper. We performed various experiments on various types of images under different conditions and confirmed the effectiveness of our image enhancement technique.