Multi-exposure image fusion technique is an important approach to obtain a composite image for High Dynamic Range. The key point of multi-exposure image fusion is to develop an effective feature measurement to evaluate the exposure degree of source images. This paper proposes a novel image fusion method for multi-exposure images with sparsity blur feature. In our algorithm, via the sparse representation and image decomposition, the sparsity blur descriptor is used to measure the exposure level of source image patches to obtain an initial decision map, and then the decision map is refined with gradient domain guided filtering. Experimental results demonstrate that the proposed method can be competitive with or even outperform the state-of-the-art fusion methods in terms of both subjective visual perception and objective evaluation metrics.