With the rapid improvement of technology, light-field cameras have been growing more and more popular. Disparity map plays an important role for many light-field applications in light-field systems. Despite recent advances, state-of-the-art algorithms fail to generate a precise disparity map rapidly enough for VLSI real time processing. To generate real-time and accurate disparity map has become the bottleneck for current research. Disparity estimation can be formulated as an energy minimization problem on a 2D Markov Random Fields (MRFs). Among many MRF global optimization method, belief propagation (BP) is a popular global optimization algorithm which gives high quality and has several advantages for hardware implementation and highly potential to achieve real-time processing. However, it requires high bandwidth, memory and computational costs because of costly iterative operations, the original belief propagation is computationally expensive for real-time system implementation. Tile-based BP is an efficient improved BP for hardware implementation. Boundary messages loaded from different directions have been performed with different iterations to reduce high memory and bandwidth requirements. In this thesis, we first examine variant view configurations of light fields for multi-baseline disparity estimation for central view. We analyze the computation time of data cost generation against different number of views and the performance of each kind of variant light-field configurations for multi-baseline setting. After analysis on variant light-field configurations, we introduce current techniques of belief propagation and examine the problems of existing methods. Followed by introducing a novel hardware-efficient message-update method to remove redundant computational costs during message-update based on tile-based BP. We focus on the algorithm and hardware architecture design of multi-baseline disparity estimation from light fields. At first, we analyze the hardware cost in the belief propagation system, and indicate the challenge and bottleneck in the area and latency resource requirement to preserve high-quality in light fields. We analyze the computation time in the view of software implementation. After software analysis, we analyze the latency and area of the belief propagation from the perspective of hardware implementation. And then we exploit the unique characteristics of the generalized truncated linear model of the smoothness term in the Markov random field and propose an efficient hardware-oriented message-update algorithm from the aspect of hardware implementation with corresponding message-update process element (PE). This message-update PE indeed reduce the complexity of message construction for belief propagation, and hence greatly reduce the area and cycle counts.