To perform tasks, such as detection or recognition of objects in natural environment by day or by night, it is possible to use images acquired from different sensors: natural images, thermal images from infrared sensor or images acquired during the night with light intensifier. Our goal is to improve the efficiency of operators performing these tasks by providing a synthetic image made from different sensors that will enhance the information content of each sensor. First we have to know the image characteristics we use: edges detection and spatial frequencies are statistically analyzed. They show the differences between the image sensors. Then we have to understand which information is important for the observer, from each sensor, for a given task. To obtain this knowledge, we have developed a psychovisual experiment to discriminate vehicles, by using the method of [Gosselin, Schyns, 2001, Vision Research 41, 2261-2271]. Stimuli presented to the observers are constructed by filtering the original image at different scales and multiplied by Gaussian “bubbles” that partially obscure the signal [Lelandais, Plantier, BIOSIGNALS2013, Spain]. The results of the psychovisual experiment give the number of bubbles necessary to perform the task and to determine the useful parts of vehicles for their discrimination.