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Strategies for reducing speckle noise in digital holography

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TLDR
A broad discussion about the noise issue in DH is provided, with the aim of covering the best-performing noise reduction approaches that have been proposed so far and quantitative comparisons among these approaches will be presented.
Abstract
Digital holography (DH) has emerged as one of the most effective coherent imaging technologies. The technological developments of digital sensors and optical elements have made DH the primary approach in several research fields, from quantitative phase imaging to optical metrology and 3D display technologies, to name a few. Like many other digital imaging techniques, DH must cope with the issue of speckle artifacts, due to the coherent nature of the required light sources. Despite the complexity of the recently proposed de-speckling methods, many have not yet attained the required level of effectiveness. That is, a universal denoising strategy for completely suppressing holographic noise has not yet been established. Thus the removal of speckle noise from holographic images represents a bottleneck for the entire optics and photonics scientific community. This review article provides a broad discussion about the noise issue in DH, with the aim of covering the best-performing noise reduction approaches that have been proposed so far. Quantitative comparisons among these approaches will be presented.

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Journal ArticleDOI

Speckle noise reduction strategies in laser-based projection imaging, fluorescence microscopy, and digital holography with uniform illumination, improved image sharpness, and resolution

TL;DR: In this article, the most suitable light source for the aforementioned techniques is a partially spatially coherent monochromatic light, i.e., a pseudo-thermal light source which provides uniform illumination with least speckle noise and without any spurious fringes.
Journal ArticleDOI

Optical Fireworks Based on Multifocal Three-Dimensional Color Prints.

TL;DR: In this article, a virtual 3D color object consisting of colorful focal spots in free space is reported, and the colors and corresponding "floating heights" of these spots are independently controlled via the design of 3D printed microlens profiles and heights of nanopillars that act as structural-color filters.
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Numerically Enhanced Stimulated Emission Depletion Microscopy with Adaptive Optics for Deep-Tissue Super-Resolved Imaging

TL;DR: A custom-built STED microscope with automated aberration correction that is capable of 3D super-resolution imaging through thick, highly aberrating tissue is demonstrated and a state of the art image denoising method by block-matching and collaborative filtering to numerically enhance fine object details otherwise mixed with noise and further enhance the image quality is introduced.
References
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Book

A wavelet tour of signal processing

TL;DR: An introduction to a Transient World and an Approximation Tour of Wavelet Packet and Local Cosine Bases.
Journal ArticleDOI

Scale-space and edge detection using anisotropic diffusion

TL;DR: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced, chosen to vary spatially in such a way as to encourage intra Region smoothing rather than interregion smoothing.
Journal ArticleDOI

De-noising by soft-thresholding

TL;DR: The authors prove two results about this type of estimator that are unprecedented in several ways: with high probability f/spl circ/*/sub n/ is at least as smooth as f, in any of a wide variety of smoothness measures.
Journal ArticleDOI

Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering

TL;DR: An algorithm based on an enhanced sparse representation in transform domain based on a specially developed collaborative Wiener filtering achieves state-of-the-art denoising performance in terms of both peak signal-to-noise ratio and subjective visual quality.
Proceedings ArticleDOI

A non-local algorithm for image denoising

TL;DR: A new measure, the method noise, is proposed, to evaluate and compare the performance of digital image denoising methods, and a new algorithm, the nonlocal means (NL-means), based on a nonlocal averaging of all pixels in the image is proposed.
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