Restoration of fog images is important for the deweathering issue in computer vision. The problem is ill-posed and can be regularized within a Bayesian context using a probabilistic fusion model. This paper presents a multiscale depth fusion (MDF) method for defog from a single image. A linear model representing the stochastic residual of nonlinear filtering is first proposed. Multiscale filtering results are probabilistically blended into a fused depth map based on the model. The fusion is formulated as an energy minimization problem that incorporates spatial Markov dependence. An inhomogeneous Laplacian-Markov random field for the multiscale fusion regularized with smoothing and edge-preserving constraints is developed. A nonconvex potential, adaptive truncated Laplacian, is devised to account for spatially variant characteristics such as edge and depth discontinuity. Defog is solved by an alternate optimization algorithm searching for solutions of depth map by minimizing the nonconvex potential in the random field. The MDF method is experimentally verified by real-world fog images including cluttered-depth scene that is challenging for defogging at finer details. The fog-free images are restored with improving contrast and vivid colors but without over-saturation. Quantitative assessment of image quality is applied to compare various defog methods. Experimental results demonstrate that the accurate estimation of depth map by the proposed edge-preserved multiscale fusion should recover high-quality images with sharp details.

This study indicated that (177)Lu-EDTMP was an effective and safe treatment for palliation of metastatic bone pain in patients with prostate or breast cancer. A dose of 1295 MBq (35 mCi) was sufficient for bone pain palliation therapy, and doses as high as 2590 MBq (70 mCi) were well tolerated.

These findings suggest that chronic heroin use induces long-lasting striatal DAT reductions. DAT availability remained unchanged during a 6-month period of abstinence. Treatment with Jitai appears to be effective at increasing striatal DAT availability.

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A lateral flow dual immunoassay (LFDIA) was developed for rapid quantitative detection of zearalenone (ZEN) and fumonisin B1 (FB1) in corn and wheat samples on a single test strip. Two test lines and the control line on the nitrocellulose membrane were coated with ZEN and FB1 conjugates and goat anti-mouse IgG, respectively. Colloidal gold nanoparticles were conjugated with monoclonal antibodies against ZEN or FB1. The intensity of the test lines was analyzed by a photometric strip reader to determine the concentrations of ZEN and FB1 based on the calibration curves of known concentrations versus intensity readings. Test parameters such as types of buffers, ratio of the two gold-labeled antibodies, and dilution of the sample extracts and the gold-labeled antibodies were optimized. The detection limit was 0.35 and 5.23 ng/mL for ZEN and FB1, respectively, and the corresponding detection ranges were 0.94-7.52 and 9.34-100.45 ng/mL, respectively. Spiked and natural samples were analyzed using both LFDIA and liquid chromatography-tandem mass spectrometry. The two methods had a good correlation (R(2) = 0.96). The dual quantitative LFDIA is sensitive, rapid, and easy-to-use for on-site testing of a large number of samples.

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