Recent studies have shown that biological neural systems are able to use noise and non linearities to improve the information processing which is occurred in. This thesis focus on this topic. We investigate the links between some operations of signal processing whose potentially appear in the visual system and its internal noises. A description of the human visual system and its different sources of noise is done in first chapter. We study in the second part the link between the irregular retinal sampling and the random fixational eye movements. We use a simple model of retina. For several kind of fluctuations we show that the likeness of the image projected on the model of retina and the real scene can be improved by random movements. In the third chapter we are interested in a problem which is recurrent in biological systems such as the visual system: noisy binary detection tasks. The influence of the internal noise of the first layers of neurons of the visual system on the performance of the detection tasks is characterized. To simulate the internal noise observed in biological neural networks we propose to use stochastic quantizers. A stochastic quantizer is a quantizer whitch of thresholds are perturbed randomly by threshold noises. Once again we observe that the threshold noise can improve the detection performance by decreasing the probability of error.