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Policy document mention is considered to indicate the significance and societal impact of scientific product. However, the accuracy of policy document altmetrics data needs to be evaluated to fully understand its strength and limitation. An in-depth coding analysis was conducted on sample policy documents records of Altmetric.com database. The sample consists of 2079 records from all 79 distinct policy document source platforms tracked by the database. Errors about mentioned publications in the policy documents (type A error) are found in 8% of the records, while errors about either the recorded policy documents or the mentioned publications in the altmetrics database (type B error) are found in 70% of the records. In type B error, policy document link error (5% of the records) could be attributable to the policy document website, transcription error (52% of the records) could be attributable to the third-party bibliographic data provider. These two categories of error are relatively minor and may have limited influence on altmetrics research and practices. False positive policy document mention (13% of the records), however, could be attributable to the Altmetric database and may diminish the validity of research based on the policy document altmetrics data. The underlying reasons remain to be further investigated. Considering the high complexity of extracting mentions of publications from various sources and formats of policy documents as well as its short history, Altmetric database has achieved excellent performance.

Underwater images suffer from low visibility and contrast caused by absorption and scattering, which leads to haze and some further limitations. The existing underwater single image dehazing methods cannot achieve a balance between the performance and computational complexity, and are difficult to produce satisfactory results in the regions with large distance. To overcome these problems, we propose a new underwater single image dehazing method, which includes an improved background light estimation based on the quad-tree subdivision iteration algorithm, and a novel transmission estimation method. For the background light estimation, we introduce a robust score for each region of the image, which can evaluate the region from both smoothness and color. For the transmission estimation, we propose the color space dimensionality reduction prior (CSDRP), which allows conversing an image from the three-dimensional RGB color space to a 2D color space, namely the UV color space. In the UV color space, by clustering the pixels into mounts of haze-lines and carefully setting the haze-free boundary, the transmission map can be figured out and used to produce an excellent dehazed image. Experimental results show that our method has competitive effects compared with mainstream underwater single image dehazing methods.

Underwater images suffer from low visibility caused by absorption and scattering, which leads to haze and some further limitations. To overcome these issues, we propose a novel dehaze method based on a universal observation that all pixels of most underwater images tend to distribute nearby a specific plane in RGB space. This observation is named the color space dimensionality reduction prior. By projecting all pixels to this plane, namely the UV color space, the color distribution of these pixels can be reduced from the three-dimensional space (RGB space) to a new two-dimensional space (UV space) without causing any excessive color shift. By carefully setting the haze-free boundary in UV space, we can obtain the image transmission and finally produce an excellent dehazed image. Experimental results show that our method has competitive results compared with mainstream underwater single image dehazing methods.

Underwater imaging has been increasingly employed in vision-based marine research. However, the inappropriate installation of a light source and the complex underwater environment will result in the uneven illumination and overexposure on the captured images. To address these issues, an underwater image enhancement framework for autonomous underwater vehicles platform is proposed, which consists of underwater light source optimization and illumination nonuniformity correction. The light source optimization method improves the imaging quality by computing an appropriate angle of the light casting. In this way, the center of the field of view is always well lit. In addition, an adaptive filter-based illumination correction algorithm is proposed to solve the uneven illumination caused by the artificial light source. During this process, image block segmentation and the measure of image enhancement index are applied to improve the adaptability and reduce the calculation errors of the filter parameters. A dataset with real underwater images collected under different natural conditions has been built and tested. The experimental results indicate that the proposed method is more adaptive and effective than the typical methods.