During digital microscopy of an oil painting surface it is inconvenient to analyze an entire image due to multiple defocused areas. The defocusing is usually caused by the small depth of the lens and the rough surface curve. Thus, these microscopic images in an oil painting have multiple focal points, which indicates multi-focus images. We present a multi-focus fusion synthesizing a focused image from scans based on focal direction and selection of focused places. Based on microscopic characteristics, a common scanned area of the images was defined to unify the lens multiplication. A focus index was applied to each pixel to identify well-focused pixels and generate a mapping image in the focal direction. Subsequently, a median filter was applied to the mapping image and a multifocal image was acquired based on actual pixel values obtained from the mapping image. The proposed method was utilized in analyzing oil painting samples carrying rough surface curves. The multifocal image facilitated the analysis of the oil painting surface and resulted in enhanced quality compared with other methods. The proposed method can be used to generate useful images in scientific and industrial microscopy.

This study proposed an auto focusing method for a multi-focus image in assembling lens modules in digital camera phones. A camera module in a camera phone is composed of a lens barrel, an IR glass, a lens mount, a PCB board and aspheric lenses. Alignment among the components is one of the important factors in product quality. Auto-focus is essential to adjust image quality of an IR glass in a lens holder, but there are two focal points in the captured image due to thickness of IR glass. So, sharpness, probability and a scale factor are defined to find desired focus from a multi-focus image. The sharpness is defined as clarity of an image. Probability and a scale factors are calculated using pattern matching with a registered image. The presented algorithm was applied to a lens assembly machine which has 5 axes, two vacuum chucks and an inspection system. The desired focus can be determined on the local maximum of the sharpness, the probability and the scale factor in the experiment.

This paper proposed the control algorithm for aspheric surface grinding and was verified by the experiment. The functions of the algorithm were simultaneous control of the position and interpolation of the aspheric curve. The nonlinear formula of the tool position was derived from the aspheric equations and the shape of the tool. The function was partitioned by an certain interval and the control parameters were calculated at each control section. The movement in a session was interpolated with acceleration and velocity. The position error was feed-backed by rotary encorder. The concept of feedback algorithm was correcting position error by increasing or decreasing the speed. In the experiment, two-axis machine was controlled to track the aspheric surface by the proposed algorithm. The effect of the control and process parameters was monitored. The result showed that the maximum tracking error was under sub-micro level for the concave and convex surfaces.