Research On Color Image Reconstruction Method For Agricultural Multimedia WSN

In recent years,with the fast developing of the agricultural intelligence,building a wireless sensor network to collect a single environmental parameters to monitor the agricultural production environment is unable to adapt the needs of agricultural production and life.At the same time,with the rapid development of network technology and image processing technology,the wireless media sensor network came into being.However,the large data transferred would not only lead to network node energy consumption too fast,but also lead the network congestion in the network.Therefore,it is more and more important and practical significance to use an adaptable compression and recovery algorithm to extend the working time of the network acquisition node and reduce the amount of network data transmission.In this paper,using Compressed Sensing to compressed the collected color image,then using the reconstruction algorithm to recovery the image,which reduces the amount of data transmission.Compression Sensing(CS),which is a new theory of signal compression and recovery,is divided into three steps: the sparse representation of the signal,the measurement matrix and the reconstruction algorithm.The three steps are used to compress and reconstruct the signal.This paper analyzes and introduces the sparse representation of CS theory,the mathematical principles and common methods of measurement matrix and reconstruction algorithm.At the same time,it introduces the RGB and YUV color model and the color image quality judgment method.According to these basic theories,the compression and reconstruction algorithms of color images of RGB and YUV color models based on CS theory are proposed.The agricultural color images experiment is used to verify its feasibility.At the end,these images were placed on different thinning groups,different measurement matrices,different reconstruction algorithms and different compression ratio of the comparative test,the experimental analysis.According to the experimental analysis,the following conclusions can be drawn:RGB and YUV color models of the color image compression perception is feasible.R GB and YUV color models of the color image compression and reconstruction algorithm having high recovery accuracy.Fourier sparse has a good recovery effect,and the wavelet sparse restores the image to preserve the texture details.Different sparse bases and measurement matrices have a significant effect on the accuracy of reconstruction.Sparse degree and compression rate have a great correlation with reconstruction precis ion.In the case of the same degree of sparseness,with the decrease of the compression rate,the reconstruction precision is gradually increased,but when the range of the same degree of sparse degree is increased,the precision of the reconstruction is gradually decreased the precision is no longer increased.