A hybrid of fuzzy theory and quadratic function for estimating and refining transmission map

© TÜBİTAK In photographs captured in outdoor environments, particles in the air cause light attenuation and degrade image quality. This effect is especially obvious in hazy environments. In this study, a fuzzy theory is proposed to estimate the transmission map of a single image. To overcome the problem of oversaturation in dehazed images, a quadratic-function-based method is proposed to refine the transmission map. In addition, the color vector of the atmospheric light is estimated using the top 1% of the brightest light area. Finally, the dehazed image is reconstructed using the transmission map and the estimated atmospheric light. Experimental results demonstrate that the proposed hybrid method performs better than the other existing methods in terms of color oversaturation, visibility, and quantitative evaluation

Haze and Smoke Removal for Visualization of Multispectral Images: A DNN Physics Aware Architecture

Remote sensing multispectral images are extensively used by applications in various fields. The degradation generated by haze or smoke negatively influences the visual analysis of the represented scene. In this paper, a deep neural network based method is proposed to address the visualization improvement of hazy and smoky images. The method is able to entirely exploit the information contained by all spectral bands, especially by the SWIR bands, which are usually not contaminated by haze or smoke. A dimensionality reduction of the spectral signatures or angular signatures is rapidly obtained by using a stacked autoencoders (SAE) trained based on contaminated images only. The latent characteristics obtained by the encoder are mapped to the R - G - B channels for visualization. The haze and smoke removal results of several Sentinel 2 scenes present an increased contrast and show the haze hidden areas from the initial natural color images

Site Characterization Of Surface And Sub-Surface Spatial Data In Producing Riverbank Filtration Site Suitability Map

Remote sensing, Geographic Information System (GIS) and electrical resistivity technique were used in this study to develop the site suitability map for river bank filtration (RBF) locations for a case study in Jenderam Hilir, Dengkil. A high resolution 2012 GeoEye-1 satellite image was classified into six classes using the supervised maximum likelihood classification process. The classified image was further analyzed using GIS technique such as overlaying, buffering and Boolean analysis, to identify the suitability of a RBF location area based on location, distance from the river and distance from built up area. The classified image results show that the overall accuracy is 89% with kappa statistic of 0.864. For the subsurface profile, the electrical images method was used for investigating the aquifer existence and to evaluating the extent of soil subsurface. Electrical-imaging resistivity results showed the lithology of sandy clay to sandy silt sediments at more than 3 m deep. From the inverse model of resistivity variation with depth indicated the occurrence of potential aquifer mostly in silty sand zones within the traps and below it. Based on lithology, a potential water-bearing aquifer was identified at a depth of 3 m depth which is good agreement with interpreted results. A site suitability map was developed and RBF locations were identified. The suitability map also coincides with the existing borehole location at study area

Computer Vision Sensing Systems for Structural Health Monitoring in Challenging Field Conditions

Computer vision sensing techniques enable easy-to-install and remote non-contact monitoring of structures and have great potentials in field applications. This study will develop/implement novel computer vision techniques for two sensing systems for monitoring different aspects of infrastructures in challenging field conditions. The dissertation is therefore composed of two parts: robust measurement of global multi-point structural displacements, and accurate and robust monitoring of local surface displacements/strains. Computer vision based displacement measurement has become popular in the recent decade. The first part presents InnoVision, a vision sensing system developed to address a number of challenging problems associated with applying vision sensors to the measurement of multi-point structural displacement in field conditions that are rarely comprehensively studied in the literature. The challenging problems include tracking low-contrast natural targets on the structural surface, insufficient resolution for long distance measurement, inevitable camera vibration, and image distortion due to heat haze in hot weather. Several techniques are developed in InnoVision to tackle these challenges. Laboratory and field tests are conducted to evaluate the performance of these techniques. In the second part, another vision sensing system SurfaceVision is developed for accurate and robust monitoring two-dimensional (2D) structural surface displacements/strains. Important structures, such as nuclear power plants, need the continuous inspection of surface conditions. As an alternative to the human inspection, conventional digital-image-correlation (DIC) based methods have been applied to surfaces painted with speckle patterns in a controlled environment. However, it is highly challenging for DIC methods to accurately measure displacement on natural concrete surfaces in outdoor conditions with changing illumination and weather conditions. Additionally, common surface displacement measurement is based on segmenting the surface image into small subsets and tracking each subset individually through template matching, the surface displacement thus obtained has obvious discontinuity and low spatial resolution. Therefore, for applicability in the outdoor environment, SurfaceVision is proposed for accurate and robust monitoring of surface displacements/strains. Advanced computer vision techniques are developed/implemented to enable surface displacement measurement with high continuity, spatial resolution, accuracy, and robustness. An intuitive strain calculation method is also developed for converting surface displacements into surface strains. A numerical simulation is formulated based on four-point bending tests to validate the accuracy and robustness of SurfaceVision in surface displacements. Four-point bending experiments using reinforced concrete specimens are conducted to demonstrate the performance of SurfaceVision under different cases of optical noises and its effectiveness in predicting crack formations

Implementation of Super Resolution Techniques in Geospatial Satellite Imagery

The potential for more precise land cover classifications and pattern analysis is provided by technological advancements and the growing accessibility of high-resolution satellite images, which might significantly improve the detection and quantification of land cover change for conservation.  A group of methods known as "super-resolution imaging" use generative modelling to increase the resolution of an imaging system. Super-Resolution Imaging, which falls under the category of sophisticated computer vision and image processing, has a variety of practical uses, including astronomical imaging, surveillance and security, medical imaging, and satellite imaging. As computer vision is where deep learning algorithms for super-resolution first appeared, they were mostly created on RGB images in 8-bit colour depth, where the sensor and camera are separated by a few meters. But no evaluation of these methods has been done

水中環境における光学画像の画質改善に関する研究

Since the 1960s, autonomous underwater vehicles (AUVs) and unmanned underwater vehicles (UUVs) have been used for deep-sea exploration. Sonar sensors also have been extensively used to detect and recognize objects in oceans. Although sonar sensors are suitable for long-range distance imaging, due to the principles of acoustic imaging, sonar images are low signal to noise ratio, low resolution and no colors. In order to acquire more detail information of underwater object, a short-range imaging system is required. In this situation, a photo vision sensor is used reasonably.However, the low contrast and color distortion of underwater images are still the major issues for practical applications. Therefore, this thesis will concentrate on the underwater optical images quality improvement.Although the underwater optical imaging technology has made a great progress, the recognition of underwater objects is still a challenging subject nowadays. Different from the normal images, underwater images suffer from poor visibility due to the medium scattering and light distortion. First of all, capturing good quality images in underwater circumstance is difficult, mostly due to attenuation caused by light that is reflected from a surface and is deflected and scattered by particles. Secondly, absorption substantially reduces the light energy. The random attenuation of the light mainly causes the haze appearance along with the part of the light scattered back from the water. In particular, an underwater object which 10 meters away from camera lens is almost indistinguishable because of light absorption. Furthermore, when the artificial light is employed, it can cause a distinctive footprint on the seafloor.In order to obtain high quality underwater images that can be adapted to the traditional image identification algorithms, this work aimed to construct an underwater image processing framework. Due to the special characteristic of underwater images,segment the image to several parts before directly perform a subject identification is thought an efficient way. And for obtaining a good underwater image segment result, the work to improve the quality of the image is necessary. Such work contains image enhancement, color correction and noise reduction, etc. The experiments demonstrate that the proposed methods produced visually pleasing results, and the numerical image quality assessment also proved the effectiveness of this proposal. The organization of this thesis is as follows.Chapter 1 briefly reviews the characteristics and types of acoustic imaging and optical imaging technologies in ocean. The traditional underwater imaging models and the issues of recent underwater imaging systems are also introduced.Chapter 2 describes a novel underwater image enhancement method. The transmission is estimated by the proposed dual-channel prior. Then a robust locally adaptive filter algorithm for enhancing underwater images is used. In addition, theartificial light removal method is also proposed. Compared with the traditional methods, the proposed method obtains better images.Chapter 3 presents a color correction method to recover the distorted image colors. In the experiments, the proposed method recovers the distorted colors in real-time. The color corrected images have a reasonable noise level in their dark regions, and the global contrast is also well improved.Chapter 4 describes two methods for image segmentation. The first one is the automatic clustering Weighted Fuzzy C Means (WFCM) based segmentation method. It automatically obtains a reasonable clustering result for the underwater images with simple texture. The second method is fast Active Contour Model (ACM) based image segmentation method, which dramatically improves the calculation speed. Compare with the traditional methods, the processing speed is improved by over 10 times.Chapter 5 presents the conclusions of this work, and points out some future researchdirections.九州工業大学博士学位論文 学位記番号：工博甲第398号 学位授与年月日：平成27年9月25日1 INTRODUCTION|2 IMAGE ENHANCEMENT|3 COLOR CORRECTION|4 IMAGE SEGMENTATION|5 CONCLUSIONS九州工業大学平成27年

Peningkatan visibilitas pada penghilangan haze berbasis perbedaan warna untuk citra digital tunggal danau kawah Gunung Kelud

Indonesia has recorded 127 active volcanoes. One of the most active volcanoes in Indonesia, among others: Kelud mountain (East Java). Mount Kelud based on historical data eruption Indicators that mark will happen eruption or change of state at mount Kelud, such as color change of the crater lake at Kelud, and incidence of smoke from belly of Kelud which appear to surface mount. To monitor the occurrence of smoke in the crater lake of Kelud mountain, can be observed through meteorology, climatology and geophysics (BMKG) of East Java close circuit television (CCTV), which is mounted directly to the crater lake. However, the use of CCTV still relies on the visual observation capability of officers in charge of supervising stations, and the haze along the day. So the CCTV image, experiencing visual degradation. To restore the contrast of images degraded by bad weather, then do some technique to eliminate the haze

Venus Exploration Targets

The primary goal of this workshop is to identify and evaluate key locations, transects, and regions (on the surface or within the atmosphere) for future exploration of planet Venus. Appropriate candidate targets include those requiring landers, atmospheric probes, gliders, or balloons, and orbital missions.institutional support, Universities Space Research Association (USRA) ... [and others] ; conveners, Virgil L. Sharpton, Larry Esposito, Christophe Sotin ; scientific organizing committee, Virgil L. Sharpton ... [and others] ; [compiled Meeting and Publication Services, Lunar and Planetary Institute]PARTIAL CONTENTS: Mapping the Surface Composition of Venus in the Near Infrared / J. Helbert, N. Müller, S. Ferrari, D. Dyar, S. Smrekar, J.W. Head, and L. Elkins-Tanton--Cleopatra Crater, a Circular Portal to the Soul of Venus / R.R. Herrick--Selection of Landing Sites for the Venera-D Mission / M.A. Ivanov, A.T. Basilevsky, J.W. Head, L.V. Zasova, and E.N. Guseva--Global Geologic Map of Venus: A Resource for Venus Exploration Planning and Site Selection / M.A. Ivanov, J.W. Head, and A.T. Basilevsky