改进稀疏表示与积化能量和的多聚焦图像融合

为解决多聚焦图像融合算法中细节信息保留受限的问题,提出改进稀疏表示与积化能量和的多聚焦图像融合算法。首先,对源图像采用非下采样剪切波变换,得到低频子带系数和高频子带系数。接着,通过滑动窗口技术从低频子带系数中提取图像块,构造联合局部自适应字典,利用正交匹配追踪算法计算得到稀疏表示系数,利用方差能量加权规则得到融合后的稀疏系数,再通过反向滑动窗口技术获得融合后的低频子带系数;然后,对于高频子带系数提出积化能量和的融合规则,得到融合后高频子带系数；最后,通过逆变换获得融合图像。实验结果表明,该算法能保留更详细的细节信息,在视觉质量和客观评价上具有一定的优势。

Multi-focus image fusion with improved sparse representation and integrated energy sum

In order to solve the problem of limited retention of detail information in the multi-focus image fusion algorithm,a multi-focus image fusion algorithm with improved sparse representation and integrated energy sum is proposed.Firstly,the non-subsampled shearlet transform is used on the source image to obtain low-frequency and high-frequency coefficient matrix.Secondly,the image block is extracted from the low-frequency coefficient matrix through the sliding window technique,a joint local adaptive dictionary is constructed,and the sparse representation coefficients are calculated using the orthogonal matching tracking algorithm.Then,the sparse after fusion is obtained using the variance energy weighting rule coefficients,and the fused low-frequency coefficient matrix is obtained through the reverse sliding window technique.Thirdly,for the high-frequency coefficients,the integration rule of the integrated energy sum is proposed to obtain the fused high-frequency coefficient matrix.Finally,the fusion image is obtained by inverse transformation.The experimental results show that the algorithm can retain more detailed information and has certain advantages in visual quality and objective evaluation.