We propose a new method to quantify the clarity of autofocused images by integrating multiple measures of images in both far- and near-focused regions. Generally, the clarity of images varies in a hill-shaped manner with respect to the lens position within a focus region. However, the variation in the far-focused regions is subject to disturbances and not very useful for the design of a focusing control. Using the properties of regional monotonous variations, the proposed method integrates additional measures, which are adequate to quantify the clarity of images in the far-focused region. The properties of monotonous variations of the measures are used to characterize the design process. The proposed focusing algorithm implements a step-size adaptation mechanism that adjusts the step size of lens motion based on the monotonous variation properties. To improve the focusing speed in the far-focused regions, the monotonous variations of multiple measures are combined to approximate the proximity of the lens to the focus position and the corresponding step size. The combined information about monotonous variations is also used to check and correct inconsistencies of the lens motion, which could occur due to motion imaging disturbances. This inconsistency check has proven to be a significant improvement on the robustness of the proposed focusing control. Four case studies demonstrate the feasibility of this algorithm and the effects of modifying individual design parameters, leading to a response time of . This speed is relatively sufficient for general industrial applications, such as LCD panel manufacturing. With the application of the inconsistency check, each of the four cases was run 100 times, and there were no cases of failure; this passes the manufacturing standard of less than 1% failure. The proposed design concept of regional monotonous variation, along with the inconsistency check and step-size adaptation, improves speed of focusing control and reliability.