Ramp distribution-based image enhancement techniques and its applications

Abstract

Thanks to the proliferation of digital imaging devices such as cameras and smartphones, taking and sharing photos have become a daily routine. According to people's desire to get high-quality images and vendors' effort to provide a high quality of experience, a number of image-enhancing techniques have been actively studied. Considering the fact that contrast is an important factor in the human perception of image quality, applying the contrast enhancement (CE) method is one of the most effective ways to enhance images. Our approach to CE of images is based on natural scene statistics (NSS). We show, in this paper, that the average intensity distribution of natural images can be linearly approximated to the ramp distribution in an ordered histogram domain as the contrast increases. Based on this finding, we propose ramp distribution based image enhancement techniques for visible light images and infrared images. For CE of visible light images, we propose ramp distribution-based global and local CE algorithms. The ramp distribution-based slant thresholding (RDST) algorithm is proposed as a global CE method which uses slant thresholding in an ordered histogram domain to yield a contrast-enhanced image. Also, the ramp distribution based adaptive slant thresholding (RDAST) algorithm is proposed as a local CE method. It adaptively adjusts a slant angle of the ramp distribution in each block to suppress noise amplification in uniform regions and maximizes contrast in non-uniform regions. The RDAST also employs a scaled global modified histogram to minimize sensitivity to block size changes. Moreover, we have found that even state-of-the-art image quality assessment metrics for contrast-changed images cannot correctly evaluate the overly contrasted images. To deal with this problem, we propose the over-contrast measure (OCM) which measures the amount of over-contrast in an image. We were able to evaluate all CE algorithms more correctly with the help of OCM. The experimental results show that the proposed algorithms have better or competitive performance as well as computational efficiency. To enhance the infrared images, we propose ramp distribution-based infrared image enhancement techniques. The proposed method consists of two parts, considering the characteristics of IR images. First, the ramp-distributed histogram is incorporated into an optimization problem with a sorted histogram of the input image to calculate a modified histogram. Second, to deal with blurred effects on IR images, we propose a relative edge-strength map for high-boost filtering to suppress noise in relatively uniform regions effectively. Compared with various state-of-the-art algorithms, experimental results show that the proposed method has highly competitive performance. The proposed algorithms can be calculated in real-time and require less memory. Therefore, they can easily be implemented through FPGA or ASIC. It is expected to be widely used for consumer electronics, including smartphones in the future.