In mid- to long-range horizontal imaging applications it is quite often atmospheric turbulence which limits the performance of an electro-optical system rather than the design and quality of the system itself. Even weak or moderate turbulence conditions can suffice to cause significant image degradation, the predominant effects being image dancing and blurring. To mitigate these effects many different methods have been proposed, most of which use either a hardware approach, such as adaptive optics, or a software approach. A great number of these methods are highly specialized with regard to input data, e.g. aiming exclusively at very short exposure images or at infrared data. So far, only a very limited number of these methods are concerned specifically with the restoration of RGB colour video. Beside motion compensation and deblurring, contrast enhancement plays a vital part in many turbulence mitigation schemes. While most contrast enhancement techniques, such as Contrast Limited Adaptive Histogram Equalization (CLAHE) work quite well on monochrome data or single colour frames, they tend to amplify noise in a colour video stream disproportionately, especially in scenes with low contrast. Therefore, in this paper the impact of different colour spaces (RGB, LAB, HSV) on the application of such typical image enhancement techniques is discussed and evaluated with regard to suppressing temporal noise as well as to their suitability for use in software-based turbulence mitigation algorithms.