This paper proposes a single-channel blind separation scheme using double blur correlation for blurring permuted images. The permuted image is blurred again and the correlation coefficients of image blocks are estimated before and after double blurring. Using the estimated correlation coefficients, a mapping space is constructed. The permutation mixing matrixes can be accurately estimated by classifying the parameters in
the mapping space, thus the source images can be separated. Simulation results show high detection accuracy for permuted images with various blurring operations. The proposed method has good robustness against Gaussian noise and lossy JPEG compression.