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This paper presents a novel low-resolution phosphene visualization of depth and boundary computed by a two-layer Associative Markov Random Fields. Unlike conventional methods modeling the depth and boundary as an individual MRF respectively, our algorithm proposed a two-layer associative MRFs framework by combining the depth with geometry-based surface boundary estimation, in which both variables are inferred globally and simultaneously. With surface boundary integration, the experiments demonstrates three significant improvements as: 1) eliminating depth ambiguities and increasing the accuracy, 2) providing comprehensive information of depth and boundary for human navigation under low-resolution phosphene vision, 3) when integrating the boundary clues into downsampling process, the foreground obstacle has been clearly enhanced and discriminated from the surrounding background. In order to gain higher efficiency and lower computational cost, the work is initialized on segmentation based depth plane fitting and labeling, and then applying the latest projected graph cut for global optimization. The proposed approach has been tested on both Middlebury and indoor real-scene data set, and achieves a much better performance with significant accuracy than other popular methods in both regular and low resolutions.