Perceptual contrast enhancement of dark images based on textural coefficients
Yakun Chang, Cheolkon Jung · 2016
We propose perceptual contrast enhancement of dark images based on textural coefficients. The textural coefficient indicates textural degree of intensity and adaptively stretches the dynamic range in an image. First, we calculate gray level difference between a central pixel and its adjacent ones. Because some differences are obviously noticeable by human eyes, we only use unnoticeable differences to obtain the textural coefficient. We apply the just noticeable difference (JND) of the human visual system (HVS) to obtain the proper threshold. Then, we apply a Gaussian kernel to texture coefficients for avoiding excessive differences between adjacent ones. Finally, we perform optimal contrast tone mapping to obtain a mapping function. Experimental results show that the proposed method successfully enhances dark regions while avoiding over-enhancement in bright regions without halo artifact and tone distortion.