Image definition assessment based on Tchebichef moments for micro-imaging

This paper proposes a Tchebichef moment (TM)-based image definition assessment (IDA) method that employs the difference in the logarithmic spectra (DLS). To avoid the influence of the original image, the essential element point spread function (PSF) is extracted from the DLS to characterize the IDA function uniquely. The amplification of the PSF spot radius to the defocus amount in the micro-imaging system enhances the featural differences among the DLSs, thereby improving the sensitivity to the defocus amount. The DLS with an obvious geometric feature variation is described by a TM with a low order, which improves the anti-noise performance. The performed simulation and experiment verified the superiority of the proposed method.