Combined the advantages of time-frequency separation of complex shearlet (CST) with the feature of guided filtering, a new image fusion algorithm based on CST domain and guided filtering is proposed. Firstly, CST is utilized for decomposition of the source images. Secondly, two scale guided filtering fusion rule is applied to the low frequency coefficients. Thirdly, larger sum-modified-Laplacian with guided filtering fusion rule is applied to the high frequency coefficients. Finally, the fused image is gained by the inverse CST. The algorithm can not only preserve the information of the source images well, but also improve the spatial continuity of fusion image. Experimental results show that the proposed method is superior to other current popular ones both in subjective visual and objective performance.

Abstract-Among the most serious limitations facing the success of future consumer gas identification systems are the drift problem and the real-time detection due to the slow response of most of today's gas sensors. This paper shows that the combination of an integrated sensor array and a Gaussian mixture model permits success in gas identification problems. An integrated sensor array has been designed with the aim of combustion gases identification. Our identification system is able to quickly recognize gases with more than 96% accuracy. Robust detection is introduced through a drift counteraction approach based on extending the training data set using a simulated drift.

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