A Research on Single Image Dehazing Algorithms Based on Dark Channel Prior

In the field of computer and machine vision, haze and fog lead to image degradation through various degradation mechanisms including but not limited to contrast attenuation, blurring and pixel distortions. This limits the efficiency of machine vision systems such as video surveillance, target tracking and recognition. Various single image dark channel dehazing algorithms have aimed to tackle the problem of image hazing in a fast and efficient manner. Such algorithms rely upon the dark channel prior theory towards the estimation of the atmospheric light which offers itself as a crucial parameter towards dehazing. This paper studies the state-of-the-art in this area and puts forwards their strengths and weaknesses. Through experiments the efficiencies and shortcomings of these algorithms are shared. This information is essential for researchers and developers in providing a reference for the development of applications and future of the research field.

A Novel Dark-Channel Dehazing Algorithm Based on Adaptive-Filter Enhanced SSR Theory

Low visibility in foggy days results in less contrasted and blurred images with color distortion which adversely affects and leads to the sub-optimal performances in image and video monitoring systems. The causes of foggy image degradation were explained in detail and the approaches of image enhancement and image restoration for defogging were introduced. The study proposed an enhanced and advanced form of the improved Retinex theory-based dehazing algorithm. The proposed algorithm achieved novel in the manner in which the dark channel prior was efficiently combined with the dark-channel prior into a single dehazing framework. The proposed approach performed the first stage in dehazing within the dark channel domain through implementation with an adaptive filter. This novel approach allowed for the dark channel features to be efficiently refined and boosted, a scheme, which according to the obtained results, significantly improved dehazing results in later stages. Experimental results showed that this approach did little to trade-off dehazing speed for efficiency. This makes the proposed algorithm a strong candidate for real-time systems due to its capability to realize efficient dehazing at considerably rapid speeds. Finally, experimental results were provided to validate the superior performance and efficiency of the proposed dehazing algorithm.

Effects of p-type GaN thickness on optical properties of Ga N-based light-emitting diodes

The influence of p-type Ga N(p Ga N) thickness on the light output power(LOP) and internal quantum efficiency(IQE) of light emitting diode(LED) was studied by experiments and simulations. The LOP of Ga N-based LED increases as the thickness of p Ga N layer decreases from 300 nm to 100 nm, and then decreases as the thickness decreases to 50 nm. The LOP of LED with 100-nm-thick pG a N increases by 30.9% compared with that of the conventional LED with 300-nm-thick p Ga N. The variation trend of IQE is similar to that of LOP as the decrease of Ga N thickness. The simulation results demonstrate that the higher light efficiency of LED with 100-nm-thick p Ga N is ascribed to the improvements of the carrier concentrations and recombination rates.

GaN-based high-voltage light-emitting diodes with backside reflector

High-voltage light-emitting diodes （HV-LED） with backside reflector, including Ti305/SiO2 distributed Bragg reflector （DBR） or hybrid reflector combining DBR and Al or Ag metal layer, are investigated using Monte Carlo ray tracing method. The hybrid reflector leads to more enhancement of light-extraction efficiency （LEE）. Moreover, the LEE can also be improved by redesigning the thicknesses of DBR. HV-LED with four redesigned DBR pairs （4-MDBR）, and those with a hybrid reflector combining 4-MDBR and Al metal layer （4-MDBR-Al）, are fabricated. Compared to 4-MDBR, the enhancement of light-output power induced by 4-MDBR-A1 is 4.6%, which is consistent with the simulated value of 4.9%.

GaN-based light-emitting diodes with hybrid micro/nano-textured indium-tin-oxide layer

Three types of textured indium-tin-oxide （ITO） surface, including nano-texturing and hybrid micro/nano-texturing with micro-holes （concave-hybrid-pattem） or micro-pillars （convex-hybrid-pattern）, were applied to GaN-based light-emitting diodes （LEDs）. The nano-texturing was realized by maskless wet-etching, and the micro-texturing was achieved by standard photolithography and wet-etching. Compared to LED chips with flat ITO surface, those with nano-pattern, concave-hybrid-pattern, and convex-hybrid-pattern exhibit enhancement of 11.3%, 15.8%, and 17.9%, respectively, for the light-output powers at 20 mA. The electrical performance has no degradation. Moreover, the convex-hybrid-pattern show higher light-output efficiency under small injection current, while the concave-hybrid-pattern exhibit better light-output efficiency at large injection current. The light- extraction efficiency is simulated by use of two-dimensional finite difference time domain method, and the numer- ical results are consistent with the experiments.