In view of the fact that the mountainous area is easy to accumulate clouds and haze， it brings difficulties to the interpretation of remote sensing images. In addition， the remote sensing image contains a large amount of information and the calculation rate is slow. In the process of dehazing， aiming at the problem of poor removal effect when the amount of haze is too much. A dehaze algorithm for optical remote sensing images that combines curvature filtering optimization and non-local dehaze method is proposed. First， curvature filter is used to reduce the total energy of the images， thereby reducing the memory occupied by the images and improving the calculation efficiency. Then the Hough transform voting mechanism is used to screen out the corresponding atmospheric light value， and the transmittance is estimated according to the atmospheric scattering model. Finally， the image reconstruction method is used to obtain the dehazed image. The experimental results show that the calculation rate of the proposed method is significantly faster than the method without Gaussian curvature filtering optimization. In terms of dehazing effect， the restored images are more detailed and clearer when the amount of haze is large， and the brightness is appropriate， which improves the effect of dehazing.