Accurate shape from focus based on focus adjustment in optical microscopy

Shim, Seong-O; Malik, Aamir Saeed; Choi, Tae‐Sun

doi:10.1002/jemt.20662

Cited by 25 publications

(10 citation statements)

References 14 publications

(17 reference statements)

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“…In another work, Ahmad and Choi [1] proposed the use of Dynamic Programming (DP) to obtain an accurate FIS by using less computations. Shim et al [33] proposed 3D shape recovery method by utilizing focus values from depth neighborhood.…”

Section: Approximation Techniquesmentioning

confidence: 99%

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Optimal depth estimation by combining focus measures using genetic programming

Mahmood

Majid

Choi

2011

Information Sciences

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Section: Approximation Techniquesmentioning

confidence: 99%

“…The SFF technique has been successfully utilized in many industrial applications, i.e. microelectronics [29], industrial inspection [33], medical diagnostics [5], 3D cameras [23], TFT-LCD color filter manufacturing [2], and comparison of polymers [26].…”

Section: Introductionmentioning

confidence: 99%

Optimal depth estimation by combining focus measures using genetic programming

Mahmood

Majid

Choi

2011

Information Sciences

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“…Although the shape-from-focus technique has already been applied to many industrial applications, such as medical imaging systems, industrial inspection, 3-D object modeling, surveillance systems, and microelectronics [5,7,8,[10][11][12][13], it is still a challenge to deal with natural images in some real-time applications, like entertainment applications in consumer electronics. In such circumstances, there could be a lot of low-texture regions in the images and only a small number of image frames can be used in order to fit the real-time requirement.…”

Section: Shape-from-focus Depth Reconstruction With Spatial Consistenmentioning

confidence: 99%

Shape-From-Focus Depth Reconstruction With a Spatial Consistency Model

Tseng

Wang²

2014

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-This paper presents a Maximum-A-Posteriori (MAP) framework to incorporate a spatial consistency prior model for depth reconstruction in the shape-from-focus (SFF) process. Existing SFF techniques, which reconstruct a dense 3-D depth from multi-focus image frames, usually have poor performance over low-contrast regions and usually need a large number frames to achieve satisfactory results. To overcome these problems, a new depth reconstruction process is proposed to estimate the depth values by solving a MAP estimation problem with the inclusion of a spatial consistency model. This consistency model assumes that within a local region the depth value of each pixel can be roughly predicted by an affine transformation of the image features at that pixel. A local learning process is proposed to construct the consistency model directly from the multi-focus image sequence. By adopting this model, the depth values can be inferred in a more robust way, especially over low-contrast regions. Besides, to improve the computational efficiency, a cell-based version of the MAP framework is proposed. Experimental results demonstrate the effective improvement in accuracy and robustness as compared with existing approaches over real and synthesized image data. Besides, experimental results also demonstrate that the proposed method can achieve quite impressive performance even only with the use of a few image frames. Index Terms-shape from focus (SFF), depth estimation, depth map, 3-D reconstruction. 1051-8215 (c)

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“…Generally, the result of median and LULU are comparable for different impulse noise densities. In future, we plan to test LULU filters for various applications like 3D recovery [10], microscopic applications [11], communication [12], medical imaging [13], [14], etc.…”

Section: =[I(ij-1) I(ij) I(i+1j-1) I(i+1j)]mentioning

confidence: 99%

Comparison of LULU and Median Filter for Image Denoising

Rahmat¹,

Malik²,

Kamel³

2013

IJCEE

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Abstract-This paper presents a comparison between LULU and Median filters for impulse noise in images. Noise removal from images is always a challenging area of research. Different methods are being used for different image noises such as Wiener filter for Gaussian noise, Frost filter for speckle noise and median filter for impulse noise. LULU filters are widely being used for impulse noise too. LULU filters are nonlinear rank selector operators which are computationally more competent and the performance of the operator is straightforward to describe. The comparison between Median and LULU filters, in this paper, is performed using two different image quality metrics which are RMSE and PSNR.Index Terms-LULU filters, impulse noise, RMSE, PSNR. I. INTRODUCTIONIn the field of image processing, there are so many issues and complexities for the recovery of original data from the noise corrupted data. In general, image noise is the variation of color or brightness intensity which can be caused by different sources. In general, image sensors degrade the quality of the images. Faulty devices, troubles with the data collection procedure, and interfering natural cause can all corrupt the data. Moreover, noise can be caused by compression and transmission errors. Thus, noise removal is required and is the first step before images are analyzed. It is necessary to apply an efficient noise reduction method to compensate for such data corruption [1], [2]. Even though there are so many different methodologies in this field for noise removal, image denoising is still a big issue. Some noise filters cause blurring of the images, because in reality the nature of the noise is unknown or only known to some extent.Impulse noise results due to the malfunctioning of the image sensors and the transmission channels. It can be identified by black and white points in the image. So far, there are so many different algorithms that had been proposed to filter this type of noise such as median, switching median, weighted median, adaptive median, rank conditioned median, separable median and contra harmonic mean filter. There are different names for impulse noise which are salt and pepper as well as shot noise.In this work, we address impulse noise. Generally, median filter is used for reducing impulse noise. We compared four different LULU filters with median filter. This comparison Manuscript received April 20, 2013; revised June 28, 2013 It is extensively applied in digital image processing due to its property of protecting edges as well as at the same time eliminating noise but it causes the image to blur. But still Median filter causes significantly a smaller amount of blurring than linear smooth filters of the same size. In general, all Median filters produce reasonable results for both bipolar and unipolar impulse noise. They are nonlinear, non-idempotent smoother which is computationally less efficient than LULU. A. 1D MedianIn one dimensional arrays, median filter is given by:The Fig. 2 illustrates the median operator in filtering a s...

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Accurate shape from focus based on focus adjustment in optical microscopy

Cited by 25 publications

References 14 publications

Optimal depth estimation by combining focus measures using genetic programming

Optimal depth estimation by combining focus measures using genetic programming

Shape-From-Focus Depth Reconstruction With a Spatial Consistency Model

Comparison of LULU and Median Filter for Image Denoising

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