Adaptive shape from focus with an error estimation in light microscopy

Abstract

Light microscopy enlarges the viewing angle while decreasing the depth of focus. This leads to mainly blurred images if the specimen being observed consists of significant high changes. In computer vision solving this problem is known as 'shape from focus'. Algorithms exist that perform both, the calculation of a sharp image and the recovery of the three dimensional structure of the specimen. In this paper three classic approaches for detecting sharp image regoins are evaluated. Three new so called focus measures are introduced and are compared to the classic approaches. A new adaptive reconstruction scheme for calculating range images as well as sharp images is presented. Experimental results on synthetic and real data demonstrate the performance of the proposed algorithm.

Review of Image Quality Measures for Solar Imaging

“…One of the methods with a very impressive performance is Helmli and Scherer's mean method (MIS5), proposed by Helmli and Scherer (2001). This technique provided very good results for the whole range of D/r 0 conditions and both types of observed solar regions.…”

“…It is noteworthy that, the original idea had to be slightly modified to enhance the method performance. On the other hand, when the seeing conditions cover a wide range, the most efficient method is Helmli and Scherer's mean (Helmli and Scherer, 2001), (MIS5). This method should be considered when observing without AO or when the seeing conditions are unknown.…”

Surface Topography and Texture Restoration from Sectional Optical Imaging by Focus Analysis

“…A subsequent approach (referred to as Focused Image Surface) locally tries to refine the initial recovered topography D by optimizing both position and orientation of 2-D planar (then curved) windows throughout the 3-D measure F so as to maximize the covered degrees of focus (Ahmad & Choi, 2005;Asif & Choi, 2001;Subbarao & Choi, 1995;Yun & Choi, 1999). Finally, the topography is often smoothed through average, median or recently bilateral filters (Helmli & Scherer, 2001;Khan et al, 2010;Niederöst et al, 2003). Interpolation techniques lying beyond the scope of this paper, only the traditional one will be used herein, sometimes finalised by a median filter.…”

Depth Estimation - An Introduction

“…This defocusing measurement is mainly done with Laplacian operators, which computes the second spatial derivative for every point in a neighbourhood of N pixels in each direction. Many other operators have been proposed, and a review of them can be found in (Helmi & Scherer, 2001). …”