关键词: 图像融合, 稀疏表示, 螺旋结构, 梯度分析

Abstract: In order to improve the effect of image fusion, an image fusion algorithm using spiral structure and gradient analysis is proposed. First, a series of high and low frequency subgraphs are obtained by nonsubsampled contourlet transform. Then, the sliding window model for the sparse representation method in the low-frequency subgraph is studied. In view of the slow fusion time, the spiral structure direction model is proposed for dictionary learning and sparse representation. In this model, the low-frequency subgraph is fused based on the rule that the sparse coefficient is determined by the largest spatial frequency, which improves the fusion efficiency. Aiming at the problem of edge mutation of the fused image in the high frequency subgraph, the high frequency fusion rule based on gradient analysis is presented to make the clearer image features easier to be retained in the final fusion image. Finally, the fusion experiments for gray and color images are carried out and the comparison and analysis between different fusion algorithms are demonstrated. The validity of the proposed algorithm, including time and fusion effects, is verified through the comparison results of subjective observation and objective data.

Key words: image fusion, sparse representation, spiral structure, gradient analysis