Super-resolution and Raman chemical imaging: From multiple low resolution images to a high resolution image

Raman spectroscopic measurements and imaging on sub-newton Berkovich and spherical imprints in fused silica

This paper lays out best practices for evaluating and optimizing a Raman spectroscopy setup to ensure the collection of reliable spectral data and/or Raman images on indented glasses. The Raman spectroscopic measurements and imaging were conducted on residual imprints created with Berkovich and spherical probes at forces in the sub-newton range in fused silica. The capability of a conventional optical instrument for mapping spectral variations in sub-newton imprints on glasses is evaluated by studying the influence of the optical configuration (choice of microscope objective) on the spatial resolution of the spectroscopy setup. The spatial resolution was quantitatively assessed in Z profile measurements and qualitatively evaluated by mapping changes in spectral features and correlated densification within the indented regions of fused silica specimens. The paper discusses the importance of appropriately matching the analysis volume of the Raman spectroscopic setup with the size of the indentation-induced densification zone by demonstrating the detrimental effects a mismatch may have on accurately capturing the magnitude of spectral changes and correlated densification.

Pre-processing Applied to Instrumental Data in Analytical Chemistry: A Brief Review of the Methods and Examples

The field of analytical chemistry has been significantly advanced by the availability of state-of-the-art instrumentation, allowing for the development of novel applications in this field. However, in many cases, the direct interpretation of the recorded data is often not straightforward, hence some level of pre-processing is required (e.g., baseline correction, derivatives, normalization, smoothing). These techniques have become a critical first step for the successful analysis of the data recorded, and it is recommended to use them before the application of chemometrics (e.g., classification, calibration development). The aim of this paper is to provide with an overview of the most used pre-processing methods applied to instrumental analytical methods (e.g., spectroscopy, chromatography). Examples of their application in near infrared and UV-VIS spectroscopy as well as in gas chromatography will be also discussed. Overall, this paper provides with a comprehensive understanding of pre-processing techniques in analytical chemistry, highlighting their importance during the analysis and interpretation of data, as well as during the development of accurate and reliable chemometric models.

High spatial resolution of topographic imaging and Raman mapping by differential correlation-confocal Raman microscopy

Confocal Raman microscopy (CRM) has found applications in many fields as a consequence of being able to measure molecular fingerprints and characterize samples without the need to employ labelling methods. However, limited spatial resolution has limited its application when identification of sub-micron features in materials is important. Here, we propose a differential correlation-confocal Raman microscopy (DCCRM) method to address this. This new method is based on the correlation product method of Raman scattering intensities acquired when the confocal Raman pinhole is placed at different (defocused) positions either side of the focal plane of the Raman collection lens. By using this correlation product, a significant enhancement in the spatial resolution of Raman mapping can be obtained. Compared with conventional CRM, these are 23.1% and 33.1% in the lateral and axial directions, respectively. We illustrate these improvements using in situ topographic imaging and Raman mapping of graphene, carbon nanotube, and silicon carbide samples. This work can potentially contribute to a better understanding of complex nanostructures in non-real time spectroscopic imaging fields.

Nanoscale Spatial Resolution in Far-Field Raman Imaging Using Hyperspectral Unmixing in Combination with Positivity Constrained Super-Resolution

This work introduces hyper-resolution (HyRes), a numerical approach for spatial resolution enhancement that combines hyperspectral unmixing and super-resolution image restoration (SRIR). HyRes yields a substantial increase in spatial resolution of Raman spectroscopy while simultaneously preserving the undistorted spectral information. The resolving power of this technique is demonstrated on Raman spectroscopic data from a polymer nanowire sample. Here, we demonstrate an achieved resolution of better than 14 nm, a more than eightfold improvement on single-channel image-based SRIR and 25× better than regular far-field Raman spectroscopy, and comparable to near-field probing techniques.

Sub-Micron Spatial Resolution in Far-Field Raman Imaging Using Positivity-Constrained Super-Resolution

Raman microscopy is a valuable tool for detecting physical and chemical properties of a sample material. When probing nanomaterials or nanocomposites the spatial resolution of Raman microscopy is not always adequate as it is limited by the optical diffraction limit. Numerical post-processing with super-resolution algorithms provides a means to enhance resolution and can be straightforwardly applied. The aim of this work is to present interior point least squares (IPLS) as a powerful tool for super-resolution in Raman imaging through constrained optimization. IPLS's potential for super-resolution is illustrated on numerically generated test images. Its resolving power is demonstrated on Raman spectroscopic data of a polymer nanowire sample. Comparison to atomic force microscopy data of the same sample substantiates that the presented method is a promising technique for analyzing nanomaterial samples.

Single Image Super Resolution Technique: An Extension to True Color Images

The super-resolution (SR) technique reconstructs a high-resolution image from single or multiple low-resolution images. SR has gained much attention over the past decade, as it has significant applications in our daily life. This paper provides a new technique of a single image super-resolution on true colored images. The key idea is to obtain the super-resolved image from observed low-resolution images. A proposed technique is based on both the wavelet and spatial domain-based algorithms by exploiting the advantages of both of the algorithms. A back projection with an iterative method is implemented to minimize the reconstruction error and for noise removal wavelet-based de-noising method is used. Previously, this technique has been followed for the grayscale images. In this proposed algorithm, the colored images are taken into account for super-resolution. The results of the proposed method have been examined both subjectively by observation of the results visually and objectively by considering the peak signal-to-noise ratio (PSNR) and mean squared error (MSE), which gives significant results and visually better in quality from the bi-cubic interpolation technique.

Noise Removal with Maintained Spatial Resolution in Raman Images of Cells Exposed to Submicron Polystyrene Particles

The biodistribution of 300 nm polystyrene particles in A549 lung epithelial cells has been studied with confocal Raman spectroscopy. This is a label-free method in which particles and cells can be imaged without using dyes or fluorescent labels. The main drawback with Raman imaging is the comparatively low spatial resolution, which is aggravated in heterogeneous systems such as biological samples, which in addition often require long measurement times because of their weak Raman signal. Long measurement times may however induce laser-induced damage. In this study we use a super-resolution algorithm with Tikhonov regularization, intended to improve the image quality without demanding an increased number of collected pixels. Images of cells exposed to polystyrene particles have been acquired with two different step lengths, i.e., the distance between pixels, and compared to each other and to corresponding images treated with the super-resolution algorithm. It is shown that the resolution after application of super-resolution algorithms is not significantly improved compared to the theoretical limit for optical microscopy. However, to reduce noise and artefacts in the hyperspectral Raman images while maintaining the spatial resolution, we show that it is advantageous to use short mapping step lengths and super-resolution algorithms with appropriate regularization. The proposed methodology should be generally applicable for Raman imaging of biological samples and other photo-sensitive samples.

Infrared chemical imaging: spatial resolution evaluation and super-resolution concept

Chemical imaging systems help to solve many challenges in various scientific fields. Able to deliver rapid spatial and chemical information, modern infrared spectrometers using Focal Plane Array detectors (FPA) are of great interest. Considering conventional infrared spectrometers with a single element detector, we can consider that the diffraction-limited spatial resolution is more or less equal to the wavelength of the light (i.e. 2.5-25 microm). Unfortunately, the spatial resolution of FPA spectroscopic setup is even lower due to the detector pixel size. This becomes a real constraint when micron-sized samples are analysed. New chemometrics methods are thus of great interest to overcome such resolution drawback, while keeping our far-field infrared imaging spectrometers. The aim of the present work is to evaluate the super-resolution concept in order to increase the spatial resolution of infrared imaging spectrometers using FPA detectors. The main idea of super-resolution is the fusion of several low-resolution images of the same sample to obtain a higher-resolution image. Applying the super-resolution concept on a relatively low number of FPA acquisitions, it was possible to observe a 30% decrease in spatial resolution.

Pharmaceutical applications of vibrational chemical imaging and chemometrics: a review

The emergence of chemical imaging (CI) has gifted spectroscopy an additional dimension. Chemical imaging systems complement chemical identification by acquiring spatially located spectra that enable visualization of chemical compound distributions. Such techniques are highly relevant to pharmaceutics in that the distribution of excipients and active pharmaceutical ingredient informs not only a product's behavior during manufacture but also its physical attributes (dissolution properties, stability, etc.). The rapid image acquisition made possible by the emergence of focal plane array detectors, combined with publication of the Food and Drug Administration guidelines for process analytical technology in 2001, has heightened interest in the pharmaceutical applications of CI, notably as a tool for enhancing drug quality and understanding process. Papers on the pharmaceutical applications of CI have been appearing in steadily increasing numbers since 2000. The aim of the present paper is to give an overview of infrared, near-infrared and Raman imaging in pharmaceutics. Sections 2 and 3 deal with the theory, device set-ups, mode of acquisition and processing techniques used to extract information of interest. Section 4 addresses the pharmaceutical applications.