Night vision for helicopter pilots is artificially enhanced by a night vision system. It consists in a light intensifier (LI) coupled with a numerical camera, and an infrared camera. The goal of my work is to improve this device by analyzing the defaults in order to correct them. The first part consists in reducing the noise level on the LI images. This requires to evaluate the nature of the noise corrupting these images, so an automatic noise estimation method has been developed. The estimation is based on a non parametric detection of homogeneous areas. Then the noise statistics are estimated using these homogeneous regions by performing a robust l1 estimation of the noise level function. The LI images can then be denoised using the noise estimation. We have developed in the second part a denoising algorithm that combines the non local means with variational methods by applying an adaptive regularization weighted by a non local data fidelity term. Then this algorithm is adapted to video denoising using the redundancy provided by the sequences, hence guaranteeing temporel stability and preservation of the fine structures. Finally, in the third part data from the optical and infrared sensors are registered. We propose an edge based multimodal registration metric. Combined with a gradient ascent resolution and a temporel scheme, the proposed method allows robust registration of the two modalities for later fusion.