Biological Random Dynamic Focusing Disparity Estimation Within Time-Sequence Images

In traditional computer stereo vision systems, a single focus is employed for capturing and processing stereo image pairs to estimate depth. However, the inherent variability of object distances in real world stereo images imposes limitations on this method, rendering it less reliable. To tackle this issue, a novel biological random dynamic focusing theory grounded in time-sequence images is proposed in this paper. In the proposed framework, the images of different focal points are captured according to the time sequence, the target is extracted and manipulated, ultimately yielding either a single focus or multi-focus image processing output depending on the application scenario. Moreover, a biological focusing disparity estimation algorithm is proposed and this theory is subsequently extended to the domain of disparity estimation, with its foundation rooted in the Semi Global Matching (SGM) algorithm. The disparity map obtained by this algorithm exhibits a more accurate disparity values and achieves a good balance between speed and accuracy. Experimental results on real world images demonstrate that our method enhanced robustness and flexibility compared to traditional methodologies.