Radial Basis Approximations Based BEMD for Enhancement of Non-Uniform Illumination Images

An image can be degraded due to many environmental factors like foggy or hazy weather,low light conditions,extra light conditions etc.Image captured under the poor light conditions is generally known as non-uniform illumination image.Non-uniform illumination hides some important information present in an image during the image capture Also,it degrades the visual quality of image which generates the need for enhancement of such images.Various techniques have been present in literature for the enhancement of such type of images.In this paper,a novel architecture has been proposed for enhancement of poor illumination images which uses radial basis approximations based BEMD(Bi-dimensional Empirical Mode Decomposition).The enhancement algorithm is applied on intensity and saturation components of image.Firstly,intensity component has been decomposed into various bi-dimensional intrinsic mode function and residue by using sifting algorithm.Secondly,some linear transformations techniques have been applied on various bidimensional intrinsic modes obtained and residue and further on joining the transformed modes with residue,enhanced intensity component is obtained.Saturation part of an image is then enhanced in accordance to the enhanced intensity component.Final enhanced image can be obtained by joining the hue,enhanced intensity and enhanced saturation parts of the given image.The proposed algorithm will not only give the visual pleasant image but maintains the naturalness of image also.

Observer-Based Intra-Cluster Lag Consensus of Multi-Agent Systems via Intermittent Adaptive Pinning Control

The intra-cluster lag consensus means that the agents in the same cluster can achieve lag consensus asymptotically while the agents in different clusters can achieve different consensus.In this paper,the authors investigate the observer-based intra-cluster lag consensus problems of multi-agent systems(MASs)with general linear dynamics and nonlinear dynamics via intermittent adaptive pinning control.The interaction network is considered to be weakly connected,i.e.,it is not necessary to be strongly connected,in-degree balanced or contain a directed spanning tree.To realise the intra-cluster lag consensus,a class of observers is designed to estimate the states of followers.Then a class of observer-based intermittent adaptive pinning control protocols is proposed according to the difference that the agents receive information source.Moreover,the pinning gains are designed to be intermittent adaptive and have an exponential convergence rate,which will effectively reduce communication costs,avoid the pinning gains being larger than those needed in real applications and guarantee the pinning gains quickly converge to steady value.Correspondingly,some sufficient consensus criteria are derived and rigorous proofs are given based on matrix theory and Lyapunov stability theory.Finally,the effectiveness for the proposed intermittent adaptive pinning control strategy is validated by a numerical simulation.