Retinex based low-light image enhancement using guided filtering and variational framework

A new image enhancement algorithm based on Retinex theory is proposed to solve the problem of bad visual effect of an image in low-light conditions. First, an image is converted from the RGB color space to the HSV color space to get the V channel. Next, the illuminations are respectively estimated by the guided filtering and the variational framework on the V channel and combined into a new illumination by average gradient. The new reflectance is calculated using V channel and the new illumination. Then a new V channel obtained by multiplying the new illumination and reflectance is processed with contrast limited adaptive histogram equalization(CLAHE). Finally, the new image in HSV space is converted back to RGB space to obtain the enhanced image. Experimental results show that the proposed method has better subjective quality and objective quality than existing methods.

Multiquadric Radial Basis Function Approximation Scheme for Solution of Total Variation Based Multiplicative Noise Removal Model

This article introduces a fastmeshless algorithm for the numerical solution nonlinear partial differential equations(PDE)by Radial Basis Functions(RBFs)approximation connected with the Total Variation(TV)-basedminimization functional and to show its application to image denoising containing multiplicative noise.These capabilities used within the proposed algorithm have not only the quality of image denoising,edge preservation but also the property of minimization of staircase effect which results in blocky effects in the images.It is worth mentioning that the recommended method can be easily employed for nonlinear problems due to the lack of dependence on a mesh or integration procedure.The numerical investigations and corresponding examples prove the effectiveness of the recommended algorithm regarding the robustness and visual improvement as well as peak-signal-to-noise ratio(PSNR),signal-to-noise ratio(SNR),and structural similarity index(SSIM)corresponded to the current conventional TV-based schemes.

FEATURES AND COMPARISONS OF THE QUASI-BIENNIAL VARIATIONS IN THE ASIA-PACIFIC MONSOON SUBSYSTEMS

The National Centers for Environmental Prediction (NCEP) reanalysis data, Climate Diagnostics Center Merged Analysis of Precipitation (CMAP) results, and NOAA Extended Reconstructed Sea Surface Temperature (SST), have been utilized in this paper to study the quasi-biennial variations in Asia-Pacific monsoon subsystems and associated SST anomalies (SSTA) and wind anomalies. Four monsoon indices are computed from NCEP/ National Center for Atmospheric Research (NCAR) reanalysis to represent the South Asian monsoon (SAM), South China Sea summer monsoon (SCSSM), Western North Pacific monsoon (WNPM) and East Asian monsoon (EAM), respectively. The quasi-biennial periods are very significant in Asia-Pacific monsoons (as discovered by power spectrum analysis), and for SAM and EAM---with moderate effects by El Ni?o-Southern Oscillation (ENSO)---the quasi-biennial periods are the most important factor. For SCSSM and WNPM (once again due to the effects of ENSO), the quasi-biennial periods are of secondary durations. There are obvious interdecadal variations in the quasi-biennial modes of the Asia-Pacific monsoon, so in the negative phase the biennial modes will not be significant or outstanding. The wind anomalies and SSTA associated with the biennial modes are very different in the SAM, WNPM and EAM regions. Since the WNPM and SCSSM are very similar in the biennial modes, they can be combined into one subsystem, called SCS/WNPM.

A Novel Particle Filtering Method for Estimation of Pulse Pressure Variation during Spontaneous Breathing

The first automatic algorithm was designed to estimate the pulse pressure variation (PPVPPV) from arterial blood pressure (ABP) signals under spontaneous breathing conditions. While currently there are a few publicly available algorithms to automatically estimate PPVPPV accurately and reliably in mechani-cally ventilated subjects, at the moment there is no automatic algorithm for estimating PPVPPV on sponta-neously breathing subjects. The algorithm utilizes our recently developed sequential Monte Carlo method (SMCM), which is called a maximum a-posteriori adaptive marginalized particle filter (MAM-PF). The performance assessment results of the proposed algorithm on real ABP signals from spontaneously breath-ing subjects were reported.