Retinal fundus photography has been widely used to diagnose various prevalent diseases because many important diseases manifest themselves on the retina. However, the quality of fundus images obtained from practical clinical environments is not always good enough for diagnosis due to uneven illumination, blurring, low contrast, etc. In this paper, we propose a simple yet efficient method for fundus image enhancement. We first conduct image decomposition to divide the input image into three layers: base, detail, and noise layers; and then illumination correction, detail enhancement and denoising are conducted respectively at these three layers. Specifically, a simple visual adaptation model is used to correct the uneven illumination at the base layer and a weighted fusion is employed to enhance details and suppress noise and artifacts. The proposed method was evaluated on public datasets (DIARETDB0 and DIARETDB1), and also on some challenging images collected by us from the hospital. In addition, quality assessments by ophthalmologists were implemented to further verify the contribution of the proposed method in helping make diagnosis. Experimental results show that the proposed method outperforms other related methods and can simultaneously handle the tasks of illumination correction, detail enhancement and noise (and artifact) suppression.