This paper presents a hierarchical baseline stereo-matching framework for depth estimation using a novelly developed light field camera. The imaging process of a micro-lens array-based light field camera is derived. A macro-pixel map is constructed by treating each micro-lens as one macro-pixel in the light field’s raw image. For each macro-pixel, a feature vector is represented by leveraging texture and gradient cues over the surrounding ring of neighboring macro-pixels. Next, the micro-lenses containing edges are detected on the macro-pixel map. Hierarchical baseline stereo-matching is performed by macro-pixel-wise coarse matching and pixel-wise fine matching, effectively eliminating matching ambiguities. Finally, a post-processing step is applied to improve accuracy. The lab-designed light field camera’s imaging performance is evaluated in terms of accuracy and processing speed by capturing real-world scenes under studio lighting conditions. And an experiment using rendered synthetic samples is conducted for quantitative evaluation, showing that depth maps with local details can be accurately recovered.