A new wavelet-based measure of image focus
TL;DR: A new measure of image focus is presented, based on wavelet transform of the image and is defined as a ratio of high-pass band and low- pass band norms, which is monotonic with respect to the degree of defocusation and sufficiently robust.
About: This article is published in Pattern Recognition Letters.The article was published on 2002-12-01. It has received 152 citations till now. The article focuses on the topics: Stationary wavelet transform & Wavelet transform.
Citations
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TL;DR: Experimental results clearly demonstrate that the proposed method outperforms the conventional fusion methods in the presence of anisotropic blur and mis-registration.
284 citations
TL;DR: The AutoPilot framework is presented, an automated method for spatiotemporally adaptive imaging that improves spatial resolution and signal strength two to five-fold, recovers cellular and sub-cellular structures in many regions that are not resolved by non-adaptive imaging, adapts to spatiotsemporal dynamics of genetically encoded fluorescent markers and robustly optimizes imaging performance during large-scale morphogenetic changes in living organisms.
Abstract: Optimal image quality in light-sheet microscopy requires a perfect overlap between the illuminating light sheet and the focal plane of the detection objective However, mismatches between the light-sheet and detection planes are common owing to the spatiotemporally varying optical properties of living specimens Here we present the AutoPilot framework, an automated method for spatiotemporally adaptive imaging that integrates (i) a multi-view light-sheet microscope capable of digitally translating and rotating light-sheet and detection planes in three dimensions and (ii) a computational method that continuously optimizes spatial resolution across the specimen volume in real time We demonstrate long-term adaptive imaging of entire developing zebrafish (Danio rerio) and Drosophila melanogaster embryos and perform adaptive whole-brain functional imaging in larval zebrafish Our method improves spatial resolution and signal strength two to five-fold, recovers cellular and sub-cellular structures in many regions that are not resolved by non-adaptive imaging, adapts to spatiotemporal dynamics of genetically encoded fluorescent markers and robustly optimizes imaging performance during large-scale morphogenetic changes in living organisms
202 citations
TL;DR: A novel iterative algorithm based on recent anisotropic denoising techniques of total variation and a Mumford-Shah functional with the EVAM restoration condition included is proposed and performs well even on very noisy images and does not require an exact estimation of mask orders.
Abstract: Blind image deconvolution is required in many applications of microscopy imaging, remote sensing, and astronomical imaging. Unfortunately, in a single-channel framework, serious conceptual and numerical problems are often encountered. An eigenvector-based method (EVAM) has been proposed for a multichannel framework which determines perfectly convolution masks in a noise-free environment if channel disparity, called co-primeness, is satisfied (see Harikumar, G. and Bresler, Y., ibid., vol.8, no.2, p.202-19, 1999; Proc. ICIP 96, vol.3, p.97-100, 1996). We propose a novel iterative algorithm based on recent anisotropic denoising techniques of total variation and a Mumford-Shah functional with the EVAM restoration condition included. A linearization scheme of half-quadratic regularization together with a cell-centered finite difference discretization scheme is used in the algorithm and provides a unified approach to the solution of total variation or Mumford-Shah. The algorithm performs well even on very noisy images and does not require an exact estimation of mask orders. We demonstrate the capabilities of the algorithm on synthetic data. Finally, the algorithm is applied to defocused images taken with a digital camera and to data from astronomical ground-based observations of the Sun.
195 citations
Cites methods from "A new wavelet-based measure of imag..."
...data, we use a wavelet-based focus measure [ 32 ] to compare results....
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...A quantitative evaluation of the amount of image blurring was done by wavelet-based focus measure [ 32 ]....
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TL;DR: A new autofocus algorithm and a new bacilli detection technique is presented with the aim to attain a high specificity rate and reduce the time consumed to analyze such sputum samples.
Abstract: Tuberculosis and other mycobacteriosis are serious illnesses which control is based on early diagnosis. A technique commonly used consists of analyzing sputum images for detecting bacilli. However, the analysis of sputum is time consuming and requires highly trained personnel to avoid high errors. Image-processing techniques provide a good tool for improving the manual screening of samples. In this paper, a new autofocus algorithm and a new bacilli detection technique is presented with the aim to attain a high specificity rate and reduce the time consumed to analyze such sputum samples. This technique is based on the combined use of some invariant shape features together with a simple thresholding operation on the chromatic channels. Some feature descriptors have been extracted from bacilli shape using an edited dataset of samples. A k-means clustering technique was applied for classification purposes and the sensitivity vs specificity results were evaluated using a standard ROC analysis procedure.
147 citations
Cites background from "A new wavelet-based measure of imag..."
...The third one is a wavelet-based measure ðMwbÞ, proposed in [16], which calculates energy in low- and high-pass bands....
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30 Apr 2004
TL;DR: It is shown that the focus measure is monotonic and unimodal with respect to image blurring and invariant to contrast changes due to the differences in the intensities of illumination.
Abstract: A new measure of image focus based on the discrete orthogonal Chebyshev moments is introduced. The low- and high-spatial-frequency components of an image can be represented as the low- and high-order Chebyshev moments, respectively. The focus measure is defined as the ratio of the norm of the high-order moments to that of low-order moments. It is shown that the focus measure is monotonic and unimodal with respect to image blurring. Additionally, it is invariant to contrast changes due to the differences in the intensities of illumination. The focus measure is tested for its discriminating power of images blurred to various degrees. Noise studies show that the focus measure is robust under Gaussian and salt-and-pepper noise. The performance of the proposed focus measure is compared with the existing focus measures.
133 citations
References
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01 May 1992TL;DR: This paper presents a meta-analyses of the wavelet transforms of Coxeter’s inequality and its applications to multiresolutional analysis and orthonormal bases.
Abstract: Introduction Preliminaries and notation The what, why, and how of wavelets The continuous wavelet transform Discrete wavelet transforms: Frames Time-frequency density and orthonormal bases Orthonormal bases of wavelets and multiresolutional analysis Orthonormal bases of compactly supported wavelets More about the regularity of compactly supported wavelets Symmetry for compactly supported wavelet bases Characterization of functional spaces by means of wavelets Generalizations and tricks for orthonormal wavelet bases References Indexes.
16,073 citations
"A new wavelet-based measure of imag..." refers methods in this paper
...…measure W using Daubechies wavelets with 2, 4, 6, and 10 taps (the number of taps denotes the number of coefficients in the wavelet filter, e.g., Haar wavelet has 2 taps) and the left-tree decomposition scheme of depth 1 and 2 (see Daubechies, 1992 for detailed description of these wavelets)....
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...To compare the influence of various wavelets and decomposition depth, we evaluated measure W using Daubechies wavelets with 2, 4, 6, and 10 taps (the number of taps denotes the number of coefficients in the wavelet filter, e.g., Haar wavelet has 2 taps) and the left-tree decomposition scheme of depth 1 and 2 (see Daubechies, 1992 for detailed description of these wavelets)....
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TL;DR: In this article, the regularity of compactly supported wavelets and symmetry of wavelet bases are discussed. But the authors focus on the orthonormal bases of wavelets, rather than the continuous wavelet transform.
Abstract: Introduction Preliminaries and notation The what, why, and how of wavelets The continuous wavelet transform Discrete wavelet transforms: Frames Time-frequency density and orthonormal bases Orthonormal bases of wavelets and multiresolutional analysis Orthonormal bases of compactly supported wavelets More about the regularity of compactly supported wavelets Symmetry for compactly supported wavelet bases Characterization of functional spaces by means of wavelets Generalizations and tricks for orthonormal wavelet bases References Indexes.
14,157 citations
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TL;DR: The shape from focus method presented here uses different focus levels to obtain a sequence of object images and suggests shape fromfocus to be an effective approach for a variety of challenging visual inspection tasks.
Abstract: The shape from focus method presented here uses different focus levels to obtain a sequence of object images. The sum-modified-Laplacian (SML) operator is developed to provide local measures of the quality of image focus. The operator is applied to the image sequence to determine a set of focus measures at each image point. A depth estimation algorithm interpolates a small number of focus measure values to obtain accurate depth estimates. A fully automated shape from focus system has been implemented using an optical microscope and tested on a variety of industrial samples. Experimental results are presented that demonstrate the accuracy and robustness of the proposed method. These results suggest shape from focus to be an effective approach for a variety of challenging visual inspection tasks. >
1,248 citations
"A new wavelet-based measure of imag..." refers background or methods in this paper
...Several focus measures have been reported in the literature (Krotkov, 1987; Nayar and Nakagawa, 1994; Subbarao et al., 1993; Subbarao and Choi, 1995; Subbarao and Tyan, 1998; Zhang and Wen, 2000)....
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...Nayar and Nakagawa (1994) did the same but used M4 instead of M2....
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TL;DR: A method is described for selecting the optimal focus measure with respect to gray-level noise from a given set of focus measures in passive autofocusing and depth-from-focus applications based on two new metrics that have been defined for estimating the noise-sensitivity of different focus measures.
Abstract: A method is described for selecting the optimal focus measure with respect to gray-level noise from a given set of focus measures in passive autofocusing and depth-from-focus applications. The method is based on two new metrics that have been defined for estimating the noise-sensitivity of different focus measures. The first metric-the autofocusing uncertainty measure (AUM)-is useful in understanding the relation between gray-level noise and the resulting error in lens position for autofocusing. The second metric-autofocusing root-mean-square error (ARMS error)-is an improved metric closely related to AUM. AUM and ARMS error metrics are based on a theoretical noise sensitivity analysis of focus measures, and they are related by a monotonic expression. The theoretical results are validated by actual and simulation experiments. For a given camera, the optimally accurate focus measure may change from one object to the other depending on their focused images. Therefore, selecting the optimal focus measure from a given set involves computing all focus measures in the set.
339 citations
"A new wavelet-based measure of imag..." refers background in this paper
...Several focus measures have been reported in the literature (Krotkov, 1987; Nayar and Nakagawa, 1994; Subbarao et al., 1993; Subbarao and Choi, 1995; Subbarao and Tyan, 1998; Zhang and Wen, 2000)....
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...Subbarao and Tyan (1998) analysed the robustness of M1, M3 and M5....
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...1) that were examined by the following focus measures: gray-level variance M1, energy of Laplacian M5, and the proposed measure W. M1 and M5 were chosen because M1 is the simplest and most cited measure andM5 was the best measure in the comparative study (Subbarao and Tyan, 1998)....
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TL;DR: The shape of the FIS is determined by searching for a shape which maximizes a focus measure, which results in more accurate shape recovery than the traditional methods.
Abstract: A new shape-from-focus method is described which is based on a new concept, named focused image surface (FIS). FIS of an object is defined as the surface formed by the set of points at which the object points are focused by a camera lens. According to paraxial-geometric optics, there is a one-to-one correspondence between the shape of an object and the shape of its FIS. Therefore, the problem of shape recovery can be posed as the problem of determining the shape of the FIS. From the shape of FIS the shape of the object is easily obtained. In this paper the shape of the FIS is determined by searching for a shape which maximizes a focus measure. In contrast to previous literature where the focus measure is computed over the planar image detector of the camera, here the focus measure is computed over the FIS. This results in more accurate shape recovery than the traditional methods. Also, using FIS, a more accurate focused image can be reconstructed from a sequence of images than is possible with traditional methods. The new method has been implemented on an actual camera system, and the results of shape recovery and focused image reconstruction are presented. >
279 citations
"A new wavelet-based measure of imag..." refers background in this paper
...Several focus measures have been reported in the literature (Krotkov, 1987; Nayar and Nakagawa, 1994; Subbarao et al., 1993; Subbarao and Choi, 1995; Subbarao and Tyan, 1998; Zhang and Wen, 2000)....
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