A stripe-removing algorithm using low-pass filtered residual images is proposed herein to remove stripe noise in hyperspectral remote sensing images. First, a Gaussian low-pass filter is used for image filtering to obtain a low-pass filtered residual image. Then, using previously determined knowledge that the rank of the stripe noise is 1 and the details are orthogonal to the stripe noise, we employ the orthogonal subspace projection technique to separate the stripe noise from the details in a low-pass filtered residual image. Finally, the separated details are then added to the filtered image. Through continuous iteration of the above mentioned three steps, the proposed algorithm can effectively remove stripe noise and overcome image blurring issues caused by traditional low-pass filtering methods. The experimental results illustrate that the proposed algorithm can significantly improve the removal of stripe noise and preserve image information comparing with the existing stripe-removing algorithms.