Time-lapse fluorescence microscopy is a valuable technology in cell biology, but it suffers from the inherent problem of intensity inhomogeneity due to uneven illumination or camera nonlinearity, known as shading artefacts. This will lead to inaccurate estimates of single-cell features such as average and total intensity. Numerous shading correction methods have been proposed to remove this effect. In order to compare the performance of different methods, many quantitative performance measures have been developed. However, there is little discussion about which performance measure should be generally applied for evaluation on real data, where the ground truth is absent. In this paper, the state-of-the-art shading correction methods and performance evaluation methods are reviewed. We implement 10 popular shading correction methods on two artificial datasets and four real ones. In order to make an objective comparison between those methods, we employ a number of quantitative performance measures. Extensive validation demonstrates that the coefficient of joint variation (CJV) is the most applicable measure in time-lapse fluorescence images. Based on this measure, we have proposed a novel shading correction method that performs better compared to well-established methods for a range of real data tested.