This paper proposes a single image haze removal algorithm that shows a marked improvement on the color attenuation prior-based method. Through a vast number of experiments on a wide variety of images, it is discovered that there are problems in the color attenuation prior, such as color distortion and background noise, which arise due to the fact that the priors do not hold true in all circumstances. Successful resolution of these problems using the proposed algorithm shows its superior performance to other state-of-the-art methods in terms of both subjective visual quality and quantitative metrics, on both synthetic and natural hazy image datasets. The proposed algorithm also is computationally friendly, due to the use of an efficient quad-decomposition algorithm for atmospheric light estimation and a simple modified hybrid median filter for depth map refinement.

Vision-based systems operating outdoors are significantly affected by weather conditions, notably those related to atmospheric turbidity. Accordingly, haze removal algorithms, actively being researched over the last decade, have come into use as a pre-processing step. Although numerous approaches have existed previously, an efficient method coupled with fast implementation is still in great demand. This paper proposes a single image haze removal algorithm with a corresponding hardware implementation for facilitating real-time processing. Contrary to methods that invert the physical model describing the formation of hazy images, the proposed approach mainly exploits computationally efficient image processing techniques such as detail enhancement, multiple-exposure image fusion, and adaptive tone remapping. Therefore, it possesses low computational complexity while achieving good performance compared to other state-of-the-art methods. Moreover, the low computational cost also brings about a compact hardware implementation capable of handling high-quality videos at an acceptable rate, that is, greater than 25 frames per second, as verified with a Field Programmable Gate Array chip. The software source code and datasets are available online for public use.

We proposed wideband antireflective circular polarizer for realizing a true black state in all viewing directions in organic light-emitting-diode displays (OLEDs). Present commercialized wideband circular polarizer consisted of a half wave and a quarter wave plates having the refractive index parameter (N_z) of 1.5 in both films exhibits light leakage in the oblique viewing directions, deteriorating image quality of a black state. We evaluated N_zs of both films and proposed a new wideband antireflective circular polarizer with a perfect dark state in all viewing directions with N_z = 0.5 in both plates, which will greatly improve image quality of OLEDs.

The light efficiency of most well‐known nematic liquid crystal (LC) modes, such as twisted nematic and in‐plane switching, depends only on the cell retardation value, irrespective of the cell gap and dielectric anisotropy of the LC. Interestingly, the light efficiency of a homogenously aligned nematic LC cell driven by a fringe electric field, termed the fringe‐field switching (FFS) mode, is found to be dependent on the magnitude of dielectric anisotropy, such that an LC with high dielectric anisotropy results in lower light efficiency than that of an LC with low dielectric anisotropy.

We propose a novel transflective liquid crystal display (LCD) with a periodically patterned electrode on a substrate. By optimizing the pattern size of the electrodes, the retardations which are required in reflective and transmissive modes are periodically achieved by the same voltage. The region with a small retardation is used as reflective mode and the region with a large retardation as transmissive mode. In the proposed transflective LCD, we can expect a very simple manufacturing process and a high optical performance as complex process such as a multi-cell gap process and multi driving method are not applied.

Haze is a term that is widely used in image processing to refer to natural and human-activity-emitted aerosols. It causes light scattering and absorption, which reduce the visibility of captured images. This reduction hinders the proper operation of many photographic and computer-vision applications, such as object recognition/localization. Accordingly, haze removal, which is also known as image dehazing or defogging, is an apposite solution. However, existing dehazing algorithms unconditionally remove haze, even when haze occurs occasionally. Therefore, an approach for haze density estimation is highly demanded. This paper then proposes a model that is known as the haziness degree evaluator to predict haze density from a single image without reference to a corresponding haze-free image, an existing georeferenced digital terrain model, or training on a significant amount of data. The proposed model quantifies haze density by optimizing an objective function comprising three haze-relevant features that result from correlation and computation analysis. This objective function is formulated to maximize the image’s saturation, brightness, and sharpness while minimizing the dark channel. Additionally, this study describes three applications of the proposed model in hazy/haze-free image classification, dehazing performance assessment, and single image dehazing. Extensive experiments on both real and synthetic datasets demonstrate its efficacy in these applications.