Nailfold capillaroscopy (NC) is a non-invasive imaging technique employed to assess the condition of blood capillaries in the nailfold. It is particularly useful for early detection of scleroderma spectrum disorders and evaluation of Raynaud's phenomenon. While diagnosis based on NC is typically performed by manual inspection, computerised nailfold capillaroscopy can help to reduce the inherent ambiguity present in human judgement while greatly reducing the time for diagnosis. A crucial step in automated image analysis not only of NC images but of any kind of medical images is an image enhancement process. In this paper, we evaluate the performance of six image enhancement/noise removal techniques for NC images, as a pre-cursor to edge detection aimed at identifying capillaries. Results on a variety of NC images show that bilateral filters and enhancers provide the best image quality for subsequent capillary detection.