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Light-field cameras attract great attention because of its refocusing and perspective-shifting functions after capturing. The special 4D-structured data contains depth information. In this paper, a novel depth estimation algorithm is proposed for light-field cameras by fully exploiting the characteristics of 4D light-field data. A novel tensor, intensity range of pixels within a microlens, is proposed, which presents strong correlation with the transition on focus, especially for texture-complex regions. Meanwhile, the other tensor, defocus blur amount is utilized to estimate the focus level, which generates more accurate depth estimation especially for homogeneous regions. Then, the depths calculated from the two tensors are fused according to the variation scale of intensity range and the minimal defocus blur amount under spatial smoothness constraints. Compared with the representative approaches, the depth generated by the proposed approach presents richer details for texture regions and higher consistency for unified regions.