Two-stage method to suppress speckle noise in digital holography

Leng, Junmin; Zhou, Jinhe; Lang, Xiaoping; Li, Xiaoying

doi:10.1007/s10043-015-0119-0

Cited by 11 publications

(10 citation statements)

References 16 publications

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“…Therefore, it performs well when the variance of the area is low. Leng et al [122] employed this filter for the speckle denoising in the images reconstructed from digital holograms.…”

Section: Local Denoising Methodsmentioning

confidence: 99%

Speckle denoising techniques in imaging systems

Kumar

Tounsi

Kaur³

et al. 2020

J. Opt.

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In digital coherent imaging and measurement systems, the image/fringe pattern quality is severely degraded by multiplicative uncorrelated noise, called speckle, resulting due to the coherent nature of the light source, which restricts the development of several applications of these systems in various fields. Digital holography, holographic interferometry, speckle metrology, and shearography are the most revealing non-invasive imaging and measurement tools in many areas including scientific, engineering, biomedical, and industrial applications. However, the presence of undesired speckle noise in the processed image/phase distribution may lead to ambiguity in the observation or analysis of the associated physical parameters, and thus, limit their applications in many fields. To overcome this problem, a variety of algorithms/methods/techniques have been developed to eliminate the inherent speckle-noise introduced in the images/phase distribution obtained from these systems, depending on the requirement and the understanding of the noise characteristics. The speckle denoising is the pre-processing step in these systems and their performance determines the accuracy of the measurand physical parameter. This review article aims to cover some important state-of-art speckle denoising algorithms. For the purpose of demonstration, the performance and comprehensive study of these algorithms are evaluated on simulation and experimental data.

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“…Therefore, it performs well when the variance of the area is low. Leng et al [122] employed this filter for the speckle denoising in the images reconstructed from digital holograms.…”

Section: Local Denoising Methodsmentioning

confidence: 99%

Speckle denoising techniques in imaging systems

Kumar

Tounsi

Kaur³

et al. 2020

J. Opt.

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“…In research on speckle noise, previous works have evaluated denoising algorithms in two ways [21,22,23]. Subjective evaluations concern how the textural details and visual effect of an image are improved after denoising.…”

Section: Theoretical Analysismentioning

confidence: 99%

Applying Speckle Noise Suppression to Refractive Indices Change Detection in Porous Silicon Microarrays

Ren

Jia

Yang

et al. 2019

Sensors

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The gray value method can be used to detect gray value changes of each unit almost parallel to the surface image of PSi (porous silicon) microarrays and indirectly measure the refractive index changes of each unit. However, the speckles of different noise intensities produced by lasers on a porous silicon surface have different effects on the gray value of the measured image. This results in inaccurate results of refractive index changes obtained from the change in gray value. Therefore, it is very important to reduce the influence of speckle noise on measurement results. In this paper, a new algorithm based on the concepts of probability-based nonlocal-means filtering (PNLM), gradient operator, and median filtering is proposed for gray value restoration of porous silicon microarray images. A good linear relationship between gray value change and refractive index change is obtained, which can reduce the influence of speckle noise on the gray value of the PSi microarray image, improving detection accuracy. This means the method based on gray value change detection can be applied to the biological detection of PSi microarray arrays.

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“…To suppress these negative factors, numerical methods (image processing) [30,[46][47][48][49][50][51][52][53][54] and optical [55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70] solutions have been developed. Numerical methods are usually based on digital filters and/or iterative procedures, such as average value subtraction [46], median filtering [47], window frequency filters [48], non-linear filtering of a single quadrant [49], wavelet filter [50], Lee and Frost filters [51], meanmedian filtering [52], non-local means [53], block matching 3D (BM3D) filtering [54], etc [30].…”

Section: Introductionmentioning

confidence: 99%

“…Incoherent/partially coherent illumination [55][56][57] and multiple uncorrelated hologram registration [58][59][60][61][62][63][64][65][66][67][68][69] are the main optical solutions. The first group makes it possible to achieve significant quality improvement.…”

Section: Introductionmentioning

confidence: 99%

“…However, these methods usually have limitations in the depth of a 3D-scene. The most common group of optical solutions records a set of uncorrelated holograms under different conditions (multilook methods), such as illumination angle changing [58,59], object [60,61] or camera [62] shifting/rotating, multiple polarizations [63,64], wavelengths [65] or phase shifts [66], implementation of phase delays by diffusers [67][68][69] or spatial light modulators [70], and multimode laser use [71]. The holograms are then reconstructed and averaged into a single object image.…”

Section: Introductionmentioning

confidence: 99%

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An optical-digital method of noise suppression in digital holography

Cheremkhin

Evtikhiev²,

Kozlov³

et al. 2022

J. Opt.

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Digital holography is widely used for the fast and accurate reconstruction of objects and 3D-scenes characteristics: intensity, depth locations, density, temperature, refractive index, etc. There are various types of noise in digital holography, including speckle noise, zero order and twin image, camera shot noise and fixed-pattern noise. Therefore, additional hologram processing is required to obtain a high-quality reconstructed image. This paper proposes an optical-digital method of noise suppression in digital holography. First, a set of uncorrelated holograms is registered. The reconstructed images are treated as a 3D array, to which a 3D filter is then applied. A 3D median filter is used in this study; however, other 3D filters based on weighted average can be utilized also. The proposed method was verified using computer-generated and optically registered digital Fresnel holograms. Even with a small number of images, the method significantly improves the quality: the normalized standard deviation was decreased by up to 3-10 times and speckle contrast was reduced by more than six times. Registration of only ten holograms is sufficient to obtain a quality close to the maximum. The flexibility of the proposed method allows for varying the ratio of the reconstruction quality to the processing time.

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Two-stage method to suppress speckle noise in digital holography

Cited by 11 publications

References 16 publications

Speckle denoising techniques in imaging systems

Speckle denoising techniques in imaging systems

Applying Speckle Noise Suppression to Refractive Indices Change Detection in Porous Silicon Microarrays

An optical-digital method of noise suppression in digital holography

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