Hybrid-Type Transparent Organic Light Emitting Diode with High Contrast Using Switchable Windows.

Transparent organic light emitting diode (OLED) display is one of the most promising devices among next-generation information displays because of beneficial characteristics, such as self-emissive and optically clear properties. Nevertheless, in conventional transparent OLED display devices, there are serious intrinsic problems in terms of the transmittance in the dark state because of empty windows in the cell, so the contrast ratio of the transparent OLED display would be deteriorated even though it can exhibit excellent bright state. In general, the transparent mode using the OLED device applies an empty area in each pixel because an emitting device could never reveal the background image, so the transparent OLED should contain the empty area in the pixel for transparent images. This may cause the optical degradation in the dark state. To solve this problem, we propose hybrid-type transparent OLED display modes that apply a liquid crystal (LC) to the transparent window part of the empty space. In this paper, we applied two dichroic LC modes- which use an electrically controlled birefringence (ECB) mode (Heilmeier type) for the polarized mode and a cholesteric LC mode (Guest-Host mode) for the non-polarized mode-to the empty area. In each hybrid mode, we have observed optical performance, including the transmittance in the dark/bright state, contrast ratio and response time as a function of cell parameters. As a result, we confirmed that the dark state and the contrast ratio could be improved by applying the proposed modes without serious decay of the transmittance in the bright state.

Adapting a Dehazing System to Haze Conditions by Piece-Wisely Linearizing a Depth Estimator.

Haze is the most frequently encountered weather condition on the road, and it accounts for a considerable number of car crashes occurring every year. Accordingly, image dehazing has garnered strong interest in recent decades. However, although various algorithms have been developed, a robust dehazing method that can operate reliably in different haze conditions is still in great demand. Therefore, this paper presents a method to adapt a dehazing system to various haze conditions. Under this approach, the proposed method discriminates haze conditions based on the haze density estimate. The discrimination result is then leveraged to form a piece-wise linear weight to modify the depth estimator. Consequently, the proposed method can effectively handle arbitrary input images regardless of their haze condition. This paper also presents a corresponding real-time hardware implementation to facilitate the integration into existing embedded systems. Finally, a comparative assessment against benchmark designs demonstrates the efficacy of the proposed dehazing method and its hardware counterpart.

Design of a Structure for Optimized Optical Performance of a Full Colored Organic Light-Emitting Diode on a Parameter Space Map.

In general, optical properties of a top-emitting organic light-emitting diode (OLED) are dependent on the cavity effect of the OLED structure. Therefore, the optical path length of the many thin solid films in the OLED, which is strongly affected by the refractive index and thickness of each material, controls the cavity effect of the cell. In previous research, a parameter space method for optimizing the inorganic layer thickness of a red OLED structure was introduced to achieve the required bandwidth and peak wavelength. This is a simple method with high accuracy and can also be applied to red, green, and blue OLED structures. To design an OLED cell with a practical approach, however, the RGB OLED device requires the thickness of each inorganic layer and organic layer in all three R, G, and B OLED structures to be same. In this study, we applied the parameter space method to an RGB OLED device to find out and optimize the thickness of three inorganic parameters: Indium Tin Oxide (ITO), cathode, and capping layer (CPL) using the finite-difference time-domain (FDTD) method. The parameters ITO, cathode, and CPL were scanned from 18 to 21 nm, 5 to 100 nm, and 10 to 200 nm, respectively. The peak wavelength and bandwidth lines of the three spectral colors were placed on a map of the three inorganic layer thickness parameters to find the optimized points that can provide the desired optical characteristics with the same film thickness in the cell.

Automating a Dehazing System by Self-Calibrating on Haze Conditions.

Existing image dehazing algorithms typically rely on a two-stage procedure. The medium transmittance and lightness are estimated in the first stage, and the scene radiance is recovered in the second by applying the simplified Koschmieder model. However, this type of unconstrained dehazing is only applicable to hazy images, and leads to untoward artifacts in haze-free images. Moreover, no algorithm that can automatically detect the haze density and perform dehazing on an arbitrary image has been reported in the literature to date. Therefore, this paper presents an automated dehazing system capable of producing satisfactory results regardless of the presence of haze. In the proposed system, the input image simultaneously undergoes multiscale fusion-based dehazing and haze-density-estimating processes. A subsequent image blending step then judiciously combines the dehazed result with the original input based on the estimated haze density. Finally, tone remapping post-processes the blended result to satisfactorily restore the scene radiance quality. The self-calibration capability on haze conditions lies in using haze density estimate to jointly guide image blending and tone remapping processes. We performed extensive experiments to demonstrate the superiority of the proposed system over state-of-the-art benchmark methods.

Haziness Degree Evaluator: A Knowledge-Driven Approach for Haze Density Estimation.

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.

Visibility Restoration: A Systematic Review and Meta-Analysis.

Image acquisition is a complex process that is affected by a wide variety of internal and environmental factors. Hence, visibility restoration is crucial for many high-level applications in photography and computer vision. This paper provides a systematic review and meta-analysis of visibility restoration algorithms with a focus on those that are pertinent to poor weather conditions. This paper starts with an introduction to optical image formation and then provides a comprehensive description of existing algorithms as well as a comparative evaluation. Subsequently, there is a thorough discussion on current difficulties that are worthy of a scientific effort. Moreover, this paper proposes a general framework for visibility restoration in hazy weather conditions while using haze-relevant features and maximum likelihood estimates. Finally, a discussion on the findings and future developments concludes this paper.

Single-Image Visibility Restoration: A Machine Learning Approach and Its 4K-Capable Hardware Accelerator.

In recent years, machine vision algorithms have played an influential role as core technologies in several practical applications, such as surveillance, autonomous driving, and object recognition/localization. However, as almost all such algorithms are applicable to clear weather conditions, their performance is severely affected by any atmospheric turbidity. Several image visibility restoration algorithms have been proposed to address this issue, and they have proven to be a highly efficient solution. This paper proposes a novel method to recover clear images from degraded ones. To this end, the proposed algorithm uses a supervised machine learning-based technique to estimate the pixel-wise extinction coefficients of the transmission medium and a novel compensation scheme to rectify the post-dehazing false enlargement of white objects. Also, a corresponding hardware accelerator implemented on a Field Programmable Gate Array chip is in order for facilitating real-time processing, a critical requirement of practical camera-based systems. Experimental results on both synthetic and real image datasets verified the proposed method's superiority over existing benchmark approaches. Furthermore, the hardware synthesis results revealed that the accelerator exhibits a processing rate of nearly 271.67 Mpixel/s, enabling it to process 4K videos at 30.7 frames per second in real time.

Single Image Haze Removal from Image Enhancement Perspective for Real-Time Vision-Based Systems.

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.

A high transmittance color liquid crystal display mode with controllable color gamut and transparency.

In this paper, we propose a color transparent liquid crystal (LC) mode that can control the properties of the color gamut and transparency in a single panel. To achieve high transmittance in the transparent LC mode, a reactive mesogen (RM) with embedded color dichroic dyes was applied instead of a color filter. Basically, the LC mode applied a 3-terminal electrode structure to switch between the transparent LC mode and the conventional color LC mode. Depending on the direction of the applied voltage, we can operate both the color mode and the transparent mode in a single panel, and modulate the transparency and color purity of the cell through appropriate voltage control. In the experiments, we confirmed that the transmittance and the color gamut of the cell were 39.4% and 2% in the transparent LC mode and 14.9% and 34% in the color LC mode, respectively. Modulation of the color gamut and transparency between each LC mode are also demonstrated in the paper.