A study on fast SIFT image mosaic algorithm based on compressed sensing and wavelet transform

Considering the disadvantages of massive calculation and slow speed of traditional Scale Invariant Feature Transform (SIFT) algorithm, we propose an improved image mosaic method which combines Wavelet Transform (WT) and Compressed Sensing (CS) algorithm. The method works as follows. Firstly, images are transformed with wavelet and compressed using compressed sensing technology. Then, image feature points are extracted in combination with SIFT algorithm. Finally, Sequential Similarity Detection Algorithm (SSDA) with adaptive threshold is used to fast search of image matching to find out an optimal stitching line, and a panoramic image is obtained. Experimental results demonstrate that the method realizes fast image matching, efficiently overcomes the shortcomings of heavy computation and low efficiency in the process of extracting image features, and guarantees matching accuracy and stitching efficiency, which meets the real-time requestments in machine vision system. This algorithm can be applied to image matching and stitching in the field of digital image security.