Abstract:

As a highly effective method of perceiving multimedia information, Wireless Multimedia Sensor Networks (WMSNs) has shown its potential in many areas. However, the outside interference in the monitoring environment brings severe noise to video images. Obviously, video image denoising becomes the key to ensuring the validity and reliability of WMSN video monitoring. To denoise WMSN video image, firstly, its features are analyzed and some pretreatment are done. Secondly, the KSVD algorithm is employed to adaptively train DCT dictionary for reflecting the image characteristics and reconstruct the key frame through improved BatchOMP algorithm with residual ratio as the iteration termination, while DCT dictionary is adopted to sparsely denoise the residual frames. Finally, the video image is reconstructed under the situation of low SNR. Experimental results show that, compared with its counterparts, the superiorities of the algorithm can be observed in both visual and some numerical guidelines, showing the suitability for the WMSN video image denoising in low SNR.

Key words: sparse denoising;K-SVD;residual ratio;low SNR;wireless multimedia sensor network