Single Image Deraining Using Residual Channel Attention Networks

Image deraining is a highly ill-posed problem.Although significant progress has been made due to the use of deep convolutional neural networks,this problem still remains challenging,especially for the details restoration and generalization to real rain images.In this paper,we propose a deep residual channel attention network(DeRCAN)for deraining.The channel attention mechanism is able to capture the inherent properties of the feature space and thus facilitates more accurate estimations of structures and details for image deraining.In addition,we further propose an unsupervised learning approach to better solve real rain images based on the proposed network.Extensive qualitative and quantitative evaluation results on both synthetic and real-world images demonstrate that the proposed DeRCAN performs favorably against state-of-the-art methods.

Localized Corrosion of Binary Mg-Ca Alloy in 0.9 wt%Sodium Chloride Solution

To further understand the localized corrosion of magnesium alloy, various in situ electrochemical techmques and ex situ electron microprobe analysis and SEM were used to monitor the corrosion process of Mg-l.0Ca alloy in 0.9 wt% sodium chloride solution. The results indicated that the localized corrosion was accompanied by the formation and thickening of a corrosion product film on the Mg-l.0Ca alloy. A localized corrosion of the alloy initiated selectively on the eutectic micro-constituent zones, then enhanced with the exposure, developed in depth with ring-shaped corrosion products accumulated around and finally formed a volcanic-like pitting. Based on the measurements, an electrochemical corrosion model was proposed accordingly to describe the formation mechanism of the volcanic-like pitting on the alloy in 0.9 wt% sodium chloride solution.

Robust optical flow estimation based on brightness correction fields

Optical flow estimation is still an important task in computer vision with many interesting applications.However,the results obtained by most of the optical flow techniques are affected by motion discontinuities or illumination changes.In this paper,we introduce a brightness correction field combined with a gradient constancy constraint to reduce the sensibility to brightness changes between images to be estimated.The advantage of this brightness correction field is its simplicity in terms of computational complexity and implementation.By analyzing the deficiencies of the traditional total variation regularization term in weakly textured areas,we also adopt a structure-adaptive regularization based on the robust Huber norm to preserve motion discontinuities.Finally,the proposed energy functional isminimized by solving its corresponding Euler-Lagrange equation in a more effective multi-resolution scheme,which integrates the twice downsampling strategy with a support-weight median filter.Numerous experiments show that our method is more effective and produces more accurate results for optical flow estimation.