{"created":"2023-05-15T11:58:09.876999+00:00","id":4077,"links":{},"metadata":{"_buckets":{"deposit":"d9126a2b-cca1-42c6-b536-064e2d5a4427"},"_deposit":{"created_by":18,"id":"4077","owners":[18],"pid":{"revision_id":0,"type":"depid","value":"4077"},"status":"published"},"_oai":{"id":"oai:kyutech.repo.nii.ac.jp:00004077","sets":["6:7"]},"author_link":["18613"],"item_20_date_granted_61":{"attribute_name":"学位授与年月日","attribute_value_mlt":[{"subitem_dategranted":"2014-03-25"}]},"item_20_degree_grantor_59":{"attribute_name":"学位授与機関","attribute_value_mlt":[{"subitem_degreegrantor":[{"subitem_degreegrantor_name":"九州工業大学"}],"subitem_degreegrantor_identifier":[{"subitem_degreegrantor_identifier_name":"17104","subitem_degreegrantor_identifier_scheme":"kakenhi"}]}]},"item_20_degree_name_58":{"attribute_name":"学位名","attribute_value_mlt":[{"subitem_degreename":"博士（工学）"}]},"item_20_description_30":{"attribute_name":"目次","attribute_value_mlt":[{"subitem_description":"Chapter 1 Introduction|Chapter 2 Multi-Source Images Fusion|Chapter 3 Laser Images Denoising|Chapter 4 Optical Image Dehazing|Chapter 5 Shallow Water De-Scattering|Chapter 6 Conclusions","subitem_description_type":"TableOfContents"}]},"item_20_description_4":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"Underwater survey systems have numerous scientific or industrial applications in the fields of geology, biology, mining, and archeology. These application fields involve various tasks such as ecological studies, environmental damage assessment, and ancient prospection. During two decades, underwater imaging systems are mainly equipped by Underwater Vehicles (UV) for surveying in water or ocean. Challenges associated with obtaining visibility of objects have been difficult to overcome due to the physical properties of the medium. In the last two decades, sonar is usually used for the detection and recognition of targets in the ocean or underwater environment. However, because of the low quality of images by sonar imaging, optical vision sensors are then used instead of it for short range identification. Optical imaging provides short-range, high-resolution visual information of the ocean floor. However, due to the light transmission’s physical properties in the water medium, the optical imaged underwater images are usually performance as poor visibility. Light is highly attenuated when it travels in the ocean. Consequence, the imaged scenes result as poorly contrasted and hazy-like obstructions. The underwater imaging processing techniques are important to improve the quality of underwater images. As mentioned before, underwater images have poor visibility because of the medium scattering and light distortion. In contrast to common photographs, underwater optical images suffer from poor visibility owing to the medium, which causes scattering, color distortion, and absorption. Large suspended particles cause scattering similar to the scattering of light in fog or turbid water that contain many suspended particles. Color distortion occurs because different wavelengths are attenuated to different degrees in water; consequently, images of ambient in the underwater environments are dominated by a bluish tone, because higher wavelengths are attenuated more quickly. Absorption of light in water substantially reduces its intensity. The random attenuation of light causes a hazy appearance as the light backscattered by water along the line of sight considerably degrades image contrast. Especially, objects at a distance of more than 10 meters from the observation point are almost unreadable because colors are faded as characteristic wavelengths, which are filtered according to the distance traveled by light in water. So, traditional image processing methods are not suitable for processing them well. This thesis proposes strategies and solutions to tackle the above mentioned problems of underwater survey systems. In this thesis, we contribute image pre-processing, denoising, dehazing, inhomogeneities correction, color correction and fusion technologies for underwater image quality improvement. The main content of this thesis is as follows. First, comprehensive reviews of the current and most prominent underwater imaging systems are provided in Chapter 1. A main features and performance based classification criterion for the existing systems is presented. After that, by analyzing the challenges of the underwater imaging systems, a hardware based approach and non-hardware based approach is introduced. In this thesis, we are concerned about the image processing based technologies, which are one of the non-hardware approaches, and take most recent methods to process the low quality underwater images. As the different sonar imaging systems applied in much equipment, such as side-scan sonar, multi-beam sonar. The different sonar acquires different images with different characteristics. Side-scan sonar acquires high quality imagery of the seafloor with very high spatial resolution but poor locational accuracy. On the contrast, multi-beam sonar obtains high precision position and underwater depth in seafloor points. In order to fully utilize all information of these two types of sonars, it is necessary to fuse the two kinds of sonar data in Chapter 2. Considering the sonar image forming principle, for the low frequency curvelet coefficients, we use the maximum local energy method to calculate the energy of two sonar images. For the high frequency curvelet coefficients, we take absolute maximum method as a measurement. The main attributes are: firstly, the multi-resolution analysis method is well adapted the cured-singularities and point-singularities. It is useful for sonar intensity image enhancement. Secondly, maximum local energy is well performing the intensity sonar images, which can achieve perfect fusion result [42]. In Chapter 3, as analyzed the underwater laser imaging system, a Bayesian Contourlet Estimator of Bessel K Form (BCE-BKF) based denoising algorithm is proposed. We take the BCE-BKF probability density function (PDF) to model neighborhood of contourlet coefficients. After that, according to the proposed PDF model, we design a maximum a posteriori (MAP) estimator, which relies on a Bayesian statistics representation of the contourlet coefficients of noisy images. The denoised laser images have better contrast than the others. There are three obvious virtues of the proposed method. Firstly, contourlet transform decomposition prior to curvelet transform and wavelet transform by using ellipse sampling grid. Secondly, BCE-BKF model is more effective in presentation of the noisy image contourlet coefficients. Thirdly, the BCE-BKF model takes full account of the correlation between coefficients [107]. In Chapter 4, we describe a novel method to enhance underwater images by dehazing. In underwater optical imaging, absorption, scattering, and color distortion are three major issues in underwater optical imaging. Light rays traveling through water are scattered and absorbed according to their wavelength. Scattering is caused by large suspended particles that degrade optical images captured underwater. Color distortion occurs because different wavelengths are attenuated to different degrees in water; consequently, images of ambient underwater environments are dominated by a bluish tone. Our key contribution is to propose a fast image and video dehazing algorithm, to compensate the attenuation discrepancy along the propagation path, and to take the influence of the possible presence of an artificial lighting source into consideration [108]. In Chapter 5, we describe a novel method of enhancing underwater optical images or videos using guided multilayer filter and wavelength compensation. In certain circumstances, we need to immediately monitor the underwater environment by disaster recovery support robots or other underwater survey systems. However, due to the inherent optical properties and underwater complex environment, the captured images or videos are distorted seriously. Our key contributions proposed include a novel depth and wavelength based underwater imaging model to compensate for the attenuation discrepancy along the propagation path and a fast guided multilayer filtering enhancing algorithm. The enhanced images are characterized by a reduced noised level, better exposure of the dark regions, and improved global contrast where the finest details and edges are enhanced significantly [109]. The performance of the proposed approaches and the benefits are concluded in Chapter 6. Comprehensive experiments and extensive comparison with the existing related techniques demonstrate the accuracy and effect of our proposed methods.","subitem_description_type":"Abstract"}]},"item_20_description_5":{"attribute_name":"備考","attribute_value_mlt":[{"subitem_description":"九州工業大学博士学位論文 学位記番号:工博甲第367号　学位授与年月日:平成26年3月25日","subitem_description_type":"Other"}]},"item_20_description_60":{"attribute_name":"学位授与年度","attribute_value_mlt":[{"subitem_description":"平成25年度","subitem_description_type":"Other"}]},"item_20_description_65":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"subitem_description":"Thesis or Dissertation","subitem_description_type":"Other"}]},"item_20_dissertation_number_62":{"attribute_name":"学位授与番号","attribute_value_mlt":[{"subitem_dissertationnumber":"甲第367号"}]},"item_20_identifier_registration":{"attribute_name":"ID登録","attribute_value_mlt":[{"subitem_identifier_reg_text":"10.18997/00004071","subitem_identifier_reg_type":"JaLC"}]},"item_20_select_64":{"attribute_name":"査読の有無","attribute_value_mlt":[{"subitem_select_item":"yes"}]},"item_20_text_34":{"attribute_name":"アドバイザー","attribute_value_mlt":[{"subitem_text_value":"芹川, 聖一"}]},"item_20_version_type_63":{"attribute_name":"出版タイプ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"open access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_abf2"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Lu, Huimin","creatorNameLang":"en"},{"creatorName":"陸, 慧敏","creatorNameLang":"ja"}],"nameIdentifiers":[{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2014-07-30"}],"displaytype":"detail","filename":"D-226_kou_k_367.pdf","filesize":[{"value":"4.5 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"D-226_kou_k_367.pdf","objectType":"fulltext","url":"https://kyutech.repo.nii.ac.jp/record/4077/files/D-226_kou_k_367.pdf"},"version_id":"bfebb47e-3ad3-474e-af50-1a1d6f0372f1"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"Underwater Imaging","subitem_subject_scheme":"Other"},{"subitem_subject":"Image Processing","subitem_subject_scheme":"Other"},{"subitem_subject":"Image Quality Improvement","subitem_subject_scheme":"Other"},{"subitem_subject":"画質改善","subitem_subject_scheme":"Other"},{"subitem_subject":"水中画像","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"doctoral thesis","resourceuri":"http://purl.org/coar/resource_type/c_db06"}]},"item_title":"Study on Image Quality Improvement Methods for Underwater Imaging Systems","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Study on Image Quality Improvement Methods for Underwater Imaging Systems","subitem_title_language":"en"},{"subitem_title":"水中イメージングシステムのための画質改善に関する研究","subitem_title_language":"ja"}]},"item_type_id":"20","owner":"18","path":["7"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2014-07-30"},"publish_date":"2014-07-30","publish_status":"0","recid":"4077","relation_version_is_last":true,"title":["Study on Image Quality Improvement Methods for Underwater Imaging Systems"],"weko_creator_id":"18","weko_shared_id":-1},"updated":"2024-01-16T01:23:53.145488+00:00"}