Robust Light Field Depth Estimation Using Occlusion-Noise Aware Data Costs

Depth estimation is essential in many light field applications. Numerous algorithms have been developed using a range of light field properties. However, conventional data costs fail when handling noisy scenes in which occlusion is present. To address this problem, we introduce a light field depth estimation method that is more robust against occlusion and less sensitive to noise. Two novel data costs are proposed, which are measured using the angular patch and refocus image, respectively. The constrained angular entropy cost (CAE) reduces the effects of the dominant occluder and noise in the angular patch, resulting in a low cost. The constrained adaptive defocus cost (CAD) provides a low cost in the occlusion region, while also maintaining robustness against noise. Integrating the two data costs is shown to significantly improve the occlusion and noise invariant capability. Cost volume filtering and graph cut optimization are applied to improve the accuracy of the depth map. Our experimental results confirm the robustness of the proposed method and demonstrate its ability to produce high-quality depth maps from a range of scenes. The proposed method outperforms other state-of-the-art light field depth estimation methods in both qualitative and quantitative evaluations.

1 Introduction

The 4D light field camera is a promising potential technology in image acquisition owing to its ability to capture rich information. Unlike conventional technology, it does not capture the accumulated intensity of a pixel but rather captures the intensity of each direction of light. Commercial light field cameras, such as those by Lytro  [1] and Raytrix [2], are attracting interest from both consumers and light field researchers because of their superiority to conventional light field camera arrays [3]. A light field image has wider applications than a conventional 2D image, including refocusing [4], saliency detection [5], matting [6], and editing [7]. Among the potential applications, light field depth estimation is the most active area of current research [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23].