Underwater image enhancement with global–local networks and compressed-histogram equalization

Due to the light absorption and scattering, captured underwater images usually contain severe color distortion and contrast reduction. To address the above problems, we combine the merits of deep learning and conventional image enhancement technology to improve the underwater image quality. We first propose a two-branch network to compensate the global distorted color and local reduced contrast, respectively. Adopting this global–local network can greatly ease the learning problem, so that it can be handled by using a lightweight network architecture. To cope with the complex and changeable underwater environment, we then design a compressed-histogram equalization to complement the data-driven deep learning, in which the parameters are fixed after training. The proposed compression strategy is able to generate vivid results without introducing over-enhancement and extra computing burden. Experiments demonstrate that our method significantly outperforms several state-of-the-arts in both qualitative and quantitative qualities.     

Application of fuzzy image restoration in criminal investigation

The advancement of science and technology has a positive effect on the development of law disciplines. The development of algorithms and artificial intelligence also has a certain impact on judicial practice. Image restoration is a significant technique in image processing. It aims to objectively restore the content or quality of the original image from the degraded image. Image degradation is always generated in image transmission, such as distortion, blur. In modern video surveillance system, image restoration is significant for criminal investigation. However, image restoration based on conventional filter algorithms cannot achieve satisfactory performance. Thus, we first introduce the image restoration algorithms based on different degradation model. Then, we propose some applications of fuzzy image restoration in criminal investigation. We conduct experiments on both degraded images and videos and experimental results have shown the effectiveness of fuzzy image restoration applying to the criminal investigation.     

Research on the parallelization of image quality analysis algorithm based on deep learning

Image quality assessment is an indispensable in computer vision applications, such as image classification, image parsing. With the development of Internet, image data acquisition becomes more conveniently. However, image distortion is inevitable due to imperfect image acquisition system, image transmission medium and image recording equipment. Traditional image quality assessment algorithms only focus on low-level visual features such as color or texture, which could not encode high-level features effectively. CNN-based methods have shown satisfactory results in image quality assessment. However, existing methods have problems such as incomplete feature extraction, partial image block distortion, and inability to determine scores. So in this paper, we propose a novel framework for image quality assessment based on deep learning. We incorporate both low-level visual features and high-level semantic features to better describe images. And image quality is analyzed in a parallel processing mode. Experiments are conducted on LIVE and TID2008 datasets demonstrate the proposed model can predict the quality of the distorted image well, and both SROCC and PLCC can reach 0.92 or higher.     

Generating video animation from single still image in social media based on intelligent computing

Bringing a single still image into reality is a challenging topic in computer animation because the driven and structural information in single still image is inadequate. In this paper, we present an image animating method for enhancing single still image in social media with virtual realistic and animated motions without prior information. We imitate the interaction between the active objects in an image and their neighboring passive objects. The existing actions in the image and the virtual specified force are employed to animate the active objects. Observing that the change between two subsequent motions of the active objects derives a motion tendency, we can calculate a virtual driving force based on the motion tendency. By virtue of the virtual driving force, the stochastic motion texture is used to animate the passive objects. Finally, the convolutional neural network is employed to optimize the virtual motion animations. In this way, the proposed method produces visually natural results while guaranteeing motion harmony between active objects and passive objects. To demonstrate the applicability and rationality of virtual animation driving force, our method generates several animations from still images in Social Media.     

A survey on analysis and implementation of state-of-the-art haze removal techniques

Haze is a poor-quality state described by the opalescent appearance of the atmosphere which reduces the visibility. It is caused by high concentrations of atmospheric air pollutants, such as dust, smoke and other particles that scatter and absorb sunlight. The poor visibility can result in the failure of multiple computer vision applications such as smart transport systems, image processing, object detection, surveillance etc. One of the major issues in the field of image processing is the restoration of images that are corrupted due to different degradations. Typically, the images or videos captured in the outside environment have low contrast, colour fade and restricted visibility due to suspended particles of the atmosphere that directly influence the image quality. This can cause difficulty in identifying the objects in the captured hazy images or frames. To address this problem, several image dehazing techniques have been developed in the literature, each of which has its own advantages and limitations, but effective image restoration remains a challenging task. In recent times, various learning (Machine learning & Deep learning) based methods greatly condensed the drawbacks of manual design of haze related features and reduces the difficulty in efficient restoration of images with less computational time and cost. The current state-of-the-art methods for haze free images, mainly from the last decade, are thoroughly examined in this survey. Moreover, this paper systematically summarizes the hardware implementations of various haze removal methods in real time. It is with the hope that this current survey acts as a reference for researchers in this scientific area and to provide a direction for future improvements based on current achievements.     

On the analysis of fracture mechanisms and mechanical behavior of AA5083-based tri-modal composites reinforced with 5 wt.%B4C and toughened by AA5083 and AA2024 coarse grain phases

In this article, the influence of AA2024 and AA5083 coarse grains on mechanical properties and failure mechanisms of AA5083-5wt. %B₄C tri-modal composite has been discussed. AA2024 and AA5083 powders (<100 µm) were added to mechanically milled AA5083-5 wt.%B₄C powders in 25 and 50 wt.% and the mixtures were consolidated using the hot press and hot extrusion techniques. Results indicated that by adding AA2024 and AA5083 powders as coarse grains, hardness and tensile strength of AA5083-5 wt.%B₄C composite decreased but ductility increased. Moreover, by adding AA2024 powders as coarse grains, fracture mode changed and cracks tended to grow through along AA2024/AA5083-5 wt.%B₄C interface rather than being arrested or deflected. It seemed that dislocation mobility and the interface between coarse grains and ultra-fine grains had the main role in determining the mechanical properties and failure mechanisms in tri-modal AA5083-B₄C composites.     

Enhanced photocatalytic degradation performance of organic contaminants by heterojunction photocatalyst BiVO4/TiO2/RGO and its compatibility on four different tetracycline antibiotics

Photocatalytic performance of four tetracycline antibiotics using BiVO₄/TiO₂/RGO composites was investigated. To make full use of catalysis, optimum preparation conditions involved RGO content, solution pH and hydrothermal temperature on the structure forming of BiVO₄/TiO₂/RGO composites were investigated. Subsequently, the obtained visible light-driven photocatalyst was used to degrade four kinds of tetracycline antibiotics involved tetracycline (TC), chlortetracycline (CTC), oxytetracycline (OTC) and doxycycline (DXC) for wastewater treatment. Results showed that BiVO₄/TiO₂/RGO photocatalyst exhibited excellent photocatalytic activity and high compatibility due to the enhanced separation efficiency of photo-generated carriers with high reduction and oxidation capability. The degradation process of four kinds of tetracycline antibiotics was traced and detected through identifying intermediates produced in the reaction system. And a possible catalytic mechanism for BiVO₄/TiO₂/RGO photocatalyst was put forward based on band gap structure of BiVO₄ and TiO₂.     

Low-memory and high-performance architectures for the CCSDS 122.0-B-1 compression standard

Two low-memory and high-performance architectures for the CCSDS 122.0-B-1 standard are proposed. They use novel memory organizations to reduce the total memory requirements in order to be implemented in a single FPGA device. The architectures were implemented in radiation-hardened and commercial FPGA devices. Based on the experimental results for the case of Virtex5QV radiation-hardened device, the throughput is 135 MSamples/sec for image with 12 bits/pixel and horizontal resolution up 8192 pixels. Also, the proposed architectures outperform the existing one in terms of the memory requirements and area.     

Controllable Image‐Based Transfer of Flow Phenomena

Modelling flow phenomena and their related weathering effects is often cumbersome due their dependence on the environment, materials and geometric properties of objects in the scene. Example‐based modelling provides many advantages for reproducing real textures, but little effort has been devoted to reproducing and transferring complex phenomena. In order to produce realistic flow effects, it is possible to take advantage of the widespread availability of flow images on the Internet, which can be used to gather key information about the flow. In this paper, we present a technique that allows the transfer of flow phenomena between photographs, adapting the flow to the target image and giving the user flexibility and control through specifically tailored parameters. This is done through two types of control curves: a fitted theoretical curve to control the mass of deposited material, and an extended colour map for properly adapting to the target appearance. In addition, our method filters and warps the input flow in order to account for the geometric details of the target surface. This leads to a fast and intuitive approach to easily transfer phenomena between images, providing a set of simple and intuitive parameters to control the process.