Research on Wavelet Based Autofocus Evaluation in Micro-vision

Abstract

This paper presents the construction of two kinds of focusing measure operators defined in wavelet domain. One mechanism is that the Discrete Wavelet Transform (DWT) coefficients in high frequency subbands of in-focused image are higher than those of defocused one. The other mechanism is that the autocorrelation of an in-focused image filtered through Continuous Wavelet Transform (CWT) gives a sharper profile than blurred one does. Wavelet base, scaling factor and form to get the sum of high frequency energy are the key factors in constructing the operator. Two new focus measure operators are defined through the autofocusing experiments on the micro-vision system of the workcell for micro-alignment. The performances of two operators can be quantificationally evaluated through the comparison with two spatial domain operators Brenner Function (BF) and Squared Gradient Function (SGF). The focus resolution of the optimized DWT-based operators is 14% higher than that of BF and its computational cost is 52% approximately lower than BF's. The focus resolution of the optimized CWT-based operators is 41% lower than that of SGF whereas its computational cost is approximately 36% lower than SGF's. It shows that the wavelet based autofocus measure functions can be practically used in micro-vision applications.