A Bayes-spectral-entropy-based measure of camera focus using a discrete cosine transform

In this paper we present a novel measure of camera focus based on the Bayes spectral entropy of an image spectrum. In order to estimate the degree of focus, the image is divided into non-overlapping sub-images of 8x8 pixels. Next, sharpness values are calculated separately for each sub-image and their mean is taken as a measure of the overall focus. The sub-image spectra are obtained by an 8x8 discrete cosine transform (DCT). Comparisons were made against four well-known measures that were chosen as reference, on images captured with a standard visible-light camera and a thermal camera. The proposed measure outperformed the reference measures by exhibiting a wider working range and a smaller failure rate. To assess its robustness to noise, additional tests were conducted with noisy images.

[1]  R. Kiss,et al.  In vitro motility evaluation of aggregated cancer cells by means of automatic image processing. , 1999, Cytometry.

[2]  Gregory K. Wallace,et al.  The JPEG still picture compression standard , 1992 .

[3]  K Cook,et al.  Comparison of autofocus methods for automated microscopy. , 1991, Cytometry.

[4]  C. Ortiz de Solórzano,et al.  Evaluation of autofocus functions in molecular cytogenetic analysis , 1997, Journal of microscopy.

[5]  Joan L. Mitchell,et al.  JPEG: Still Image Data Compression Standard , 1992 .

[6]  C. A. Murthy,et al.  Pattern Recognition Letters Pattern classification with genetic algorithms , 2003 .

[7]  Pierre A. Devijver,et al.  On a New Class of Bounds on Bayes Risk in Multihypothesis Pattern Recognition , 1974, IEEE Transactions on Computers.

[8]  Sung-Jea Ko,et al.  New autofocusing technique using the frequency selective weighted median filter for video cameras , 1999, 1999 Digest of Technical Papers. International Conference on Consumer Electronics (Cat. No.99CH36277).

[9]  Muralidhara Subbarao,et al.  Selecting the Optimal Focus Measure for Autofocusing and Depth-From-Focus , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Marcelo H. Ang,et al.  Practical issues in pixel-based autofocusing for machine vision , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[11]  Joewono Widjaja,et al.  Use of wavelet analysis for improving autofocusing capability , 1998 .

[12]  Chao Zhang,et al.  Estimating the amount of defocus through a wavelet transform approach , 2004, Pattern Recognit. Lett..

[13]  Sim Heng Ong,et al.  Autofocusing for tissue microscopy , 1993, Image Vis. Comput..

[14]  A W Smeulders,et al.  Robust autofocusing in microscopy. , 2000, Cytometry.

[15]  Robert A. King,et al.  The use of self-entropy as a focus measure in digital holography , 1989, Pattern Recognit. Lett..

[16]  P. Yap,et al.  Image focus measure based on Chebyshev moments , 2004 .