Nighttime image dehazing using color cast removal and dual path multi-scale fusion strategy

Nighttime image dehazing aims to remove the effect of haze on the images captured in nighttime,which however,raises new challenges such as severe color distortion,more complex lighting conditions,and lower contrast.Instead of estimating the transmission map and atmospheric light that are difficult to be accurately acquired in nighttime,we propose a nighttime image dehazing method composed of a color cast removal and a dual path multi-scale fusion algorithm.We first propose a human visual system(HVS)inspired color correction model,which is effective for removing the color deviation on nighttime hazy images.Then,we propose to use dual path strategy that includes an underexposure and a contrast enhancement path for multi-scale fusion,where the weight maps are achieved by selecting appropriate exposed areas under Gaussian pyramids.Extensive experiments demonstrate that the visual effect of the hazy nighttime images in real-world datasets can be significantly improved by our method regarding contrast,color fidelity,and visibility.In addition,our method outperforms the state-of-the-art methods qualitatively and quantitatively.

A 0.1–1.5 GHz, low jitter, area efficient PLL in 55-nm CMOS process

A 0.1-1.5 GHz, 3.07 pS root mean squares （RMS）jitter, area efficient phase locked loop （PLL） with multiphase clock outputs is presented in this paper. The size of capacitor in the low pass filter （LPF） is significantly decreased by implementing a dual path charge pump （CP） technique in this PLL. Subject to specified power con- sumption, a novel optimization method is introduced to optimize the transistor size in the voltage control oscillator （VCO）, CP and phase/frequency detector （PFD） in order to minimize clock jitter. This method could improve 3-6 dBc/Hz phase noise. The proposed PLL has been fabricated in 55 nm CMOS process with an integrated 16 pF metal-oxide-metal （MOM） capacitor, occupies 0.05 mm2 silicon area, the measured total power consumption is 2.8 mW @ 1.5 GHz and the phase noise is -102 dBc/Hz @ 1 MHz offset frequency.

Research on Image Fusion Based on Deep Adversarial Learning

Image fusion,as an important task in computer vision,essentially extracts important features from source images to complement each other and generate fusion images with higher quality and richer information.Infrared and visible images contain different information due to different imaging quantity principles.The key of infrared and visible image fusion algorithm is to integrate the thermal radiation information extracted from infrared images with the captured details and texture information of visible images,so as to obtain a fusion image with complete structure and rich detailed information.Based on the generative adversarial network model,this paper proposes an infrared and visible image fusion method based on dual path dual discriminator generating adversarial network,aiming at the problems existing in the existing research algorithms,such as inadequate extraction of feature information,low efficiency of network model feature transfer,easy loss of shallow information in single-path feature extraction,fewer fusion levels caused by sub-path feature extraction and unbalance of discriminator modes.The gradient path and contrast path based on the difference stitching of source images are constructed at the generator side to improve the detail information and contrast of fused images.The feature information of infrared and visible images is extracted by multi-scale decomposition to solve the problem of incomplete feature extraction on a single scale.Then the source image is introduced into each layer of the double-path dense network,which can improve the efficiency of feature transmission and obtain more source image information.At the end of the discriminator,a double discriminator is used to estimate the region distribution of infrared image and visible image,so as to avoid the mode imbalance problem of the loss of infrared image contrast information in the single discriminator network.Finally,we construct the master-auxiliary gradient and the master-auxiliary strength loss function to improve the information extraction ability of the network model.Compared with other image fusion methods on public data sets,the experimental results show that the proposed method achieves good results on objective evaluation indexes(mean gradient,spatial frequency,structural similarity and peak signal-to-noise ratio).