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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.

The aim of this study was to investigate the association of sarcopenia and diabetic foot disease (DFD) in a cross-sectional study. Body composition was assessed using dual-energy X-ray-absorptiometry (DXA) among 1105 patients with type 2 diabetes (120 patients with newly diagnosed DFD [DFD duration less than 3 months]). Severity of DFD was assessed, referring to foot ulcers, Wagner grade and the percentage of amputation. Skeletal muscle index (SMI) was calculated, and sarcopenia was defined as SMI less than 7.0 kg/m2 (in men) or 5.4 kg/m2 (in women). SMI was significantly decreased in patients with DFD compared to patients without (6.79 ± 1.20 vs. 7.21 ± 1.05 kg/m2, P P P = 0.027) after controlling confounders including age, diabetic duration and diabetic chronic complications. In DFD group, patients with sarcopenia exhibited more foot ulcers, higher Wagner grade and greater percentage of amputation compared to patients without sarcopenia. We conclude that sarcopenia is independently associated with DFD. Worse prognosis is seen in patients with DFD accompanied by sarcopenia.

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.

General and abdominal obesity are the major subtypes of obesity. Compared with general obesity, abdominal obesity was considered to be more closely associated with chronic diabetes complications, such as cardiovascular diseases and diabetic retinopathy (1,2). Although the association between abdominal obesity and urinary albumin was reported in previous studies (3–5), whether abdominal obesity is more closely associated with diabetic kidney disease (DKD) than general obesity has not been reported yet. Two studies were carried out to investigate the association of general and abdominal obesity with DKD. Study A was a cross-sectional study. Body composition was assessed using DXA among 1,016 patients with type 2 diabetes (T2D). General obesity parameters, including BMI, total body fat percentage (TBF), and fat mass index (FMI), and abdominal obesity parameters, including waist circumference (WC), waist-to-height ratio (WHtR), and visceral adipose tissue (VAT), were measured. DKD is defined as chronic kidney disease (CKD) stage 3–5 (estimated glomerular filtration rate [eGFR]

Background Compared with general obesity, abdominal obesity has been shown to be more closely correlated to chronic diabetic complications such as cardiovascular disease and diabetic retinopathy. The association between obesity and diabetic kidney disease is unclear. We did two independent studies, one cross-sectional study and one 5 year prospective study, to investigate the association of general and abdominal obesity with diabetic kidney disease. Methods In the cross-sectional study in a hospital in Chongqing, China, body composition was assessed with dual-energy X-ray absorptiometry in 1016 patients with type 2 diabetes. Variables measured to define general obesity were BMI, total body fat percentage, and fat-mass index (ratio of fat mass to height squared). Abdominal obesity variables were waist circumference, waist-to-height ratio, and visceral adipose tissue. Diabetic kidney disease was defined as chronic kidney disease stage 3–5 (estimated glomerular filtration rate [eGFR] 2 ). In the prospective study, 279 patients with type 2 diabetes without diabetic kidney disease at baseline were followed up for 5 years after initial assessment in a community centre. BMI, waist circumference, waist-to-height ratio, and waist-to-hip ratio were used as indicators of obesity type in the prospective study. Obesity-related variables were split into low, median, and high tertiles and patients were stratified by values accordingly for both studies. Ethical approval was granted by the Ethics Committee of the First Affiliated Hospital of Chongqing Medical University. Informed consent was provided by all participants. Multivariate logistic analysis was done to determine whether parameters of obesity could be risk factors for diabetic kidney disease and adjustments made for confounding factors. The odds ratios (ORs) of general obesity parameters and abdominal obesity parameters were calculated, with reference to the lowest tertile of those parameters; OR was 1. Findings In the cross-sectional study, 470 patients had stage 1 chronic kidney disease, 374 patients had stage 2 disease, and 172 patients had stages 3–5 disease. Participants with higher BMI (24·67 kg/m 2 [SD 3·33] in those with stage 1 chronic kidney disease vs 24·78 kg/m 2 [3·14] in those with stage 2 chronic kidney disease vs 25·40 kg/m 2 [3·18] in those with stages 3–5 chronic kidney disease; p=0·040), total body fat percentage (29·21% [6·17] vs 29·57% [6·16] vs 30·74% [6·18]; p=0·024), fat-mass index (7·31 kg/m 2 [2·21] vs 7·43 kg/m 2 [2·21] vs 7·89 kg/m 2 [2·14]; p=0·014), waist circumference (92·71 cm [9·31] vs 93·98 cm [9·08] vs 95·52 cm [8·88]; p=0·047), waist-to-height ratio (0·57 [IQR 0·54–0·61] vs 0·58 [0·55–0·62] vs 0·61 [0·56–0·65]; p 2 [43·34] vs 121·67 cm 2 [43·10] vs 133·31 cm 2 [45·01]; p=0·0022) were more likely to have a lower eGFR, compared with those with lower values. Logistic regression analyses showed that variables of general obesity were associated with a risk of diabetic kidney disease (in the median vs high tertiles, for BMI p trend =0·0371; for total body fat percentage p trend =0·044; for fat-mass index p trend =0·0053). However, there was no correlation when visceral adipose tissue was adjusted (in the median vs high tertiles, for BMI p trend =0·18; for total body fat percentage p trend =0·66; for fat-mass index p trend =0·26). In the prospective study, 41 patients developed diabetic kidney disease after 6 years follow-up. No association was noted between BMI and risk of diabetic kidney disease or in the crude or adjusted models (in the crude model 1·11 [0·49–2·50] in the median tertile vs 1·10 [0·48–2·47] in the high tertile, p=0·81; in the waist-to-height ratio-adjusted model 0·43 [0·26–1·18] vs 0·76 [0·32–1·80], p=0·24; in the multivariable model 0·44 [0·13–5·54] vs 1·33 [0·25–6·97], p=0·40). Abdominal obesity variables were associated with risk of diabetic kidney disease after adjustment for BMI (for waist circumference 1·39 [1·10–8·03] in the median tertile vs 2·59 [1·45–9·31] in the high tertile, p=0·022; for waist-to-hip ratio 2·63 [0·71–5·79] vs 5·16 [1·38–9·31], p=0·015; for waist-to-height ratio 3·35 [0·86–7·08] vs 5·96 [1·51–13·06], p=0·03). The associations between the risk of diabetic kidney disease and waist circumference, waist-to-hip ratio, waist-to-height ratio were the same after adjustment for other confounders. Interpretation Abdominal obesity is more closely associated with the risk of diabetic kidney disease than general obesity in China. Funding National Key Clinical Specialties Construction Program of China, the National Natural Science Foundation of China and The Fundamental Science and Advanced Technology Research of Chongqing.