Fast depth estimation for light field images is an important task for multiple applications such as image-based rendering and refocusing. Most previous approaches to light field depth estimation involve high computational costs. Therefore, in this study, we propose a fast depth estimation method based on multi-view stereo matching for light field images. Similar to other conventional methods, our method consists of initial depth estimation and refinement. For the initial estimation, we use a one-bit feature for each pixel and calculate matching costs by summing all combinations of viewpoints with a fast algorithm. To reduce computational time, we introduce an offline viewpoint selection strategy and cost volume interpolation. Our refinement process solves the minimization problem in which the objective function consists of ℓ ₁ data and smoothness terms. Although this problem can be solved via a graph cuts algorithm, it is computationally expensive; therefore, we propose an approximate solver based on a fast-weighted median filter. Experiments on synthetic and real-world data show that our method achieves competitive accuracy with the shortest computational time of all methods.