Single image dehazing using a new color channel

Images with hazy scene suffer from low-contrast, which reduces the visible quality of the scene, thus making object detection a more challenging task. Low-contrast can result from foggy weather conditions during image acquisition. Dehazing is a process of removal of haze from the photography of a hazy scene. Single-image dehazing based on dark channel priors are well-known techniques in this field. However, the performance of such techniques is limited to priors or constraints. Moreover, this type of method fails when images have sky-region. So, a method is proposed, which can restore the visibility of hazy images. First, a hazy image is divided into blocks of size 32 × 32, then the score of each block is calculated to select a block having the highest score. Atmospheric light is calculated from the selected block. A new color channel is considered to remove atmospheric scattering, obtained channel value and atmospheric light are then used to calculate the transmission map in the second step. Third, radiance is computed using a transmission map and atmospheric light. The illumination scaling factor is adopted to enhance the quality of a dehazed image in the final step. Experiments are performed on six datasets namely, I-HAZE, O-HAZE, BSDS500, FRIDA, RESIDE dataset and natural images from Google. The proposed method is compared against 11 state-of-the-art methods. The performance is analyzed using fourteen quantitative evaluation metrics. All the results demonstrate that the proposed method outperforms 11 state-of-the-art methods in most of the cases.     

Droplet Entrainment Analysis in a Flash Evaporator with an Image-Based Measurement Technique

Local droplet sizes and volumes of entrained liquid are captured with an image-based measurement technique for comparison with a conventional, integral method for entrainment analysis. Experiments in a forced circulation flash evaporation were performed for different operating conditions and with two different chemical systems. Droplet size and frequency rise with an increase in thermal energy input. The local readings confirm the trends found by the integral measurement method. The modification of the image-based probe enables the detection of small (≈ 10 µm) and at the same time fast droplets under challenging operating conditions, such as vacuum and superheated feed similar to industrial process conditions.     

Learning shape and texture progression for young child face aging

Face aging (FA) for young faces refers to rendering the aging faces at target age for an individual, generally under 20s, which is an important topic of facial age analysis. Unlike traditional FA for adults, it is challenging to age children with one deep learning-based FA network, since there are deformations of facial shapes and variations of textural details. To alleviate the deficiency, a unified FA framework for young faces is proposed, which consists of two decoupled networks to apply aging image translation. It explicitly models transformations of geometry and appearance using two components: GD-GAN, which simulates the Geometric Deformation using Generative Adversarial Network; TV-GAN, which simulates the Textural Variations guided by the age-related saliency map. Extensive experiments demonstrate that our method has advantages over the state-of-the-art methods in terms of synthesizing visually plausible images for young faces, as well as preserving the personalized features.     

Past is important: Improved image captioning by looking back in time

A major development in the area of image captioning consists of trying to incorporate visual attention in the design of language generative model. However, most previous studies only emphasize its role in enhancing visual composition at the current moment, while neglect its role in global sequence reasoning. This problem appears not only in captioning model, but also in reinforcement learning structure. To tackle this issue, we first propose a Visual Reserved model that enables previous visual context to be considered for the current sequence reasoning. Next, a Attentional-Fluctuation Supervised model is also proposed in reinforcement learning structure. Compared against the traditional strategies that only take non-differentiable Natural Language Processing (NLP) metrics as the incentive standard, the proposed model regards the fluctuation of previous attention matrix as an important indicator to judge the convergence of the captioning model. The proposed methods have been tested on MS-COCO captioning dataset and achieve competitive results evaluated by the evaluation server of MS COCO captioning challenge.     

Secure image encryption scheme using 4D-Hyperchaotic systems based reconfigurable pseudo-random number generator and S-Box

This paper introduces the design of a hardware efficient reconfigurable pseudorandom number generator (PRNG) using two different feedback controllers based four-dimensional (4D) hyperchaotic systems i.e. Hyperchaotic-1 and -2 to provide confidentiality for digital images. The parameter's value of these two hyperchaotic systems is set to be a specific value to get the benefits i.e. all the multiplications (except a few multiplications) are performed using hardwired shifting operations rather than the binary multiplications, which doesn't utilize any hardware resource. The ordinary differential equations (ODEs) of these two systems have been exploited to build a generic architecture that fits in a single architecture. The proposed architecture provides an opportunity to switch between two different 4D hyperchaotic systems depending on the required behavior. To ensure the security strength, that can be also used in the encryption process in which encrypt the input data up to two times successively, each time using a different PRNG configuration. The proposed reconfigurable PRNG has been designed using Verilog HDL, synthesized on the Xilinx tool using the Virtex-5 (XC5VLX50T) and Zynq (XC7Z045) FPGA, its analysis has been done using Matlab tool. It has been found that the proposed architecture of PRNG has the best hardware performance and good statistical properties as it passes all fifteen NIST statistical benchmark tests while it can operate at 79.101-MHz or 1898.424-Mbps and utilize only 0.036 %, 0.23 %, and 1.77 % from the Zynq (XC7Z045) FPGA's slice registers, slice LUTs, and DSP blocks respectively. Utilizing these PRNGs, we design two 16 × 16 substitution boxes (S-boxes). The proposed S-boxes fulfill the following criteria: Bijective, Balanced, Non-linearity, Dynamic Distance, Strict Avalanche Criterion (SAC) and BIC non-linearity criterion. To demonstrate these PRNGs and S-boxes, a new three different scheme of image encryption algorithms have been developed: a) Encryption using S-box-1, b) Encryption using S-box-2 and, c) Two times encryption using S-box-1 and S-box-2. To demonstrate that the proposed cryptosystem is highly secure, we perform the security analysis (in terms of the correlation coefficient, key space, NPCR, UACI, information entropy and image encryption quantitatively in terms of (MSE, PSNR and SSIM)).     

Far-red-emitting YAl3(BO3)4:Cr3+ phosphors with excellent thermal stability and high luminescent yield for plant growth LEDs

Phosphors-converted LEDs (pc-LEDs) are excellent artificial light sources for indoor plant cultivation, in which the far-red-emitting component (700−780 nm) plays an important role in regulating the photomorphogenesis of plants. Accordingly, highly efficient and thermally stable far-red-emitting phosphors are indispensable for developing high-performance plant cultivation pc-LEDs. Herein, far-red-emitting YAl₃(BO₃)₄:Cr³⁺ (YAB:Cr³⁺) phosphors were synthesized by solid-state reaction, and their photoluminescence characteristics, thermal quenching, quantum yield (QY), and application in pc-LEDs were systematically investigated. The YAB:Cr³⁺ phosphor has an intense broadband absorption to the blue light, simultaneously exhibiting the sharp-line ²E emission and the broadband ⁴ T₂ emission of Cr³⁺ with a QY of ~86.7%. The far-red broadband emissions of YAB:Cr³⁺ centered at ~735 nm show a high resemblance to the active-state (P_FR) absorption of plant phytochrome. Moreover, the YAB:Cr³⁺ phosphor shows the thermally enhanced luminescence at temperatures of 303−393 K and the near-zero thermal quenching up to 423 K. The anomalous thermal enhancement is attributed to the temperature-dependent repopulation between ²E and ⁴ T₂ states. Finally, a pc-LED device was fabricated with the YAB:Cr³⁺ phosphor and blue chip, exhibiting the light out power of ~50.6 mW and energy conversion efficiency of ~17.4% at 100 mA drive current, respectively. The exceptional PL features including suitable excitation/emission wavelengths, suppressed thermal quenching and high QY make YAB:Cr³⁺ phosphors very promising for applications in plant growth pc-LEDs.     

Image sequence-based risk behavior detection of power operation inspection personnel

A novel image sequence-based risk behavior detection method to achieve high-precision risk behavior detection for power maintenance personnel is proposed in this paper. In this method, the original image sequence data is first separated from the foreground and background. Then, the free anchor frame detection method is used in the foreground image to detect the personnel and correct their direction. Finally, human posture nodes are extracted from each frame of the image sequence, which are then used to identify the abnormal behavior of the human. Simulation experiment results demonstrate that the proposed algorithm has significant advantages in terms of the accuracy of human posture node detection and risk behavior identification.     

Controlled growth of Bi-Functional La doped CeO2 nanorods for photocatalytic H2 production and supercapacitor applications

The rapid increase in energy consumption has severely rehabilitated human life urging to develop reliable and environmental friendly energy storage devices. Target oriented, systematic approach has been adopted to synthesis La doped CeO₂ nanostructures with percentage as La_xCe_1-xO₂ (X = 0,1,3,5,7) for potential super capacitors applications. Morphological doping impact on H₂ production, electrochemical and optical properties are thoroughly investigated. XRD studies revealed the crystalline phase purity and attained approximately 35 nm average crystallite size. The SEM images exposed that primary morphology nano-particles has been tuned into nanorods by increasing the La concentration in CeO₂ with size range 40～60 nm. CV graphs depicted that the prepared electrodes obey the pseudo capacitive faradaic reactions behavior in nature. Maximum capacitance (925 F g^-1) has been achieved by La_0·05Ce_0·95O₂ which is better than numerous reported materials. The La_0·05Ce_0·95O₂ also exhibited excellent GCD stability with 87.8% retention exhibiting it suitability for supercapacitor applications. Furthermore, the La_0·05Ce_0·95O₂ showed the significantly higher H₂ (9 μmol h^?1g^?1) production rate as compared to undoped CeO₂ and La_0·01Ce_0·99O_2, La_0·03Ce_0·97O₂ samples. This higher production is attributed to the recombination rate and have strong substantial correlation with optical characteristics.