Traditional nonlocal means denoising algorithm calculates similarity weight between image patches using exponential functions, which cannot accurately reflect the similarity between image patches. Method noise obtained by existing twostage nonlocal means methods is unsatisfactory, and the information contained  is insufficiently used. Aiming at the problems above, we propose a novel algorithm called twostage nonlocal means denoising based on hybrid robust weight and improved method noise. Firstly, we propose to use an enhanced hybrid robust weight function to calculate the similarity between image patches. Secondly, we use the predenoised image to construct improved method noise, which is then combined with a twostage framework. Finally, the new hybrid robust weight function as well as the improved method noise is applied to the twostage nonlocal means scheme. Experimental results show that the proposed algorithm can calculate the similarity between image patches more precisely and make the best of method noise, and it has better performance in denoising and preserving structure details than traditional ones.