Coherent Raman microscopy (CRM) is a powerful nonlinear optical imaging technique based on contrast via Raman active molecular vibrations. CRM has been used in domains ranging from biology to medicine to geology in order to provide quick, sensitive, chemical-specific, and label-free 3D sectioning of samples. The Raman contrast is usually obtained by combining two ultrashort pulse input beams, known as Pump and Stokes, whose frequency difference is adjusted to the Raman vibrational frequency of interest. CRM can be used in conjunction with other imaging modalities such as second harmonic generation, fluorescence, and third harmonic generation microscopy, resulting in a multimodal imaging technique that can capture a massive amount of data. Two fundamental elements are crucial in CRM. First, a laser source which is broadband, stable, rapidly tunable, and low in noise. Second, a strategy for image analysis that can handle denoising and material classification issues in the relatively large datasets obtained by CRM techniques. Stimulated Raman Scattering (SRS) microscopy is a subset of CRM techniques, and this thesis is devoted entirely to it. Although Raman imaging based on a single vibrational resonance can be useful, non-resonant background signals and overlapping bands in SRS can impair contrast and chemical specificity. Tuning over the Raman spectrum is therefore crucial for target identification, which necessitates the use of a broadband and easily tunable laser source. Although supercontinuum generation in a nonlinear fibre could provide extended tunability, it is typically not viable for some CRM techniques, specifically in SRS microscopy. Signal acquisition schemes in SRS microscopy are focused primarily on detecting a tiny modulation transfer between the Pump and Stokes input laser beams. As a result, very low noise source is required. The primary and most important component in hyperspectral SRS microscopy is a low-noise broadband laser source. The second problem in SRS microscopy is poor signal-to-noise (SNR) ratios in some situations, which can be caused by low target-molecule concentrations in the sample and/or scattering losses in deep-tissue imaging, as examples. Furthermore, in some SRS imaging applications (e.g., in vivo), fast imaging, low input laser power or short integration time is required to prevent sample photodamage, typically resulting in low contrast (low SNR) images. Low SNR images also typically suffer from poorly resolved spectral features. Various de-noising techniques have been used to date in image improvement. However, to enable averaging, these often require either previous knowledge of the noise source or numerous images of the same field of view (under better observing conditions), which may result in the image having lower spatial-spectral resolution. Sample segmentation or converting a 2D hyperspectral image to a chemical concentration map, is also a critical issue in SRS microscopy. Raman vibrational bands in heterogeneous samples are likely to overlap, necessitating the use of chemometrics to separate and segment them. We will address the aforementioned issues in SRS microscopy in this thesis. To begin, we demonstrate that a supercontinuum light source based on all normal dispersion (ANDi) fibres generates a stable broadband output with very low incremental source noise. The ANDi fibre output's noise power spectral density was evaluated, and its applicability in hyperspectral SRS microscopy applications was shown. This demonstrates the potential of ANDi fibre sources for broadband SRS imaging as well as their ease of implementation. Second, we demonstrate a deep learning neural net model and unsupervised machine-learning algorithm for rapid and automated de-noising and segmentation of SRS images based on a ten-layer convolutional autoencoder: UHRED (Unsupervised Hyperspectral Resolution Enhancement and De-noising). UHRED is trained in an unsupervised manner using only a single ("one-shot") hyperspectral image, with no requirements for training on high quality (ground truth) labelled data sets or images.

The First Book on CRS MicroscopyCompared to conventional Raman microscopy, coherent Raman scattering (CRS) allows label-free imaging of living cells and tissues at video rate by enhancing the weak Raman signal through nonlinear excitation. Edited by pioneers in the field and with contributions from a distinguished team of experts, Coherent Raman Sc

Stimulated Raman Scattering Microscopy: Techniques and Applications describes innovations in instrumentation, data science, chemical probe development, and various applications enabled by a state-of-the-art stimulated Raman scattering (SRS) microscope. Beginning by introducing the history of SRS, this book is composed of seven parts in depth including instrumentation strategies that have pushed the physical limits of SRS microscopy, vibrational probes (which increased the SRS imaging functionality), data science methods, and recent efforts in miniaturization. This rapidly growing field needs a comprehensive resource that brings together the current knowledge on the topic, and this book does just that. Researchers who need to know the requirements for all aspects of the instrumentation as well as the requirements of different imaging applications (such as different types of biological tissue) will benefit enormously from the examples of successful demonstrations of SRS imaging in the book. Led by Editor-in-Chief Ji-Xin Cheng, a pioneer in coherent Raman scattering microscopy, the editorial team has brought together various experts on each aspect of SRS imaging from around the world to provide an authoritative guide to this increasingly important imaging technique. This book is a comprehensive reference for researchers, faculty, postdoctoral researchers, and engineers. - Includes every aspect from theoretic reviews of SRS spectroscopy to innovations in instrumentation and current applications of SRS microscopy - Provides copious visual elements that illustrate key information, such as SRS images of various biological samples and instrument diagrams and schematics - Edited by leading experts of SRS microscopy, with each chapter written by experts in their given topics

his book has been prepared with the aim to present the application of these two state-of-the art technologies in agricultural sciences and food technology, and to explain the protocols for analyses of different plant, animal, microbiological and food samples as well as for different biotechnology procedures. Selected methods and protocols which are used in plant stress physiology, weed science, fruit breeding research, microbial ecology, plant virus and fungus diagnostics, phytobacteriology, fishery, food biochemistry, food materials and food technology are described. Special adaptation of certain protocols is required for application in each of these sciences, for every type of GMO organism, food technology raw material, and food technology product, as well as for every type of bacteria, virus, fungus or fungus-like organism, for each type of raw material in terms of plant host species, plant organs, year period and conditions in the laboratory. Application of molecular methods, primarily qPCR, and Raman microscopy/ spectroscopy in agricultural and food sciences provides substantial opportunity for increased production efficiency, food safety, better product quality and improvement of plant and animal health. This book is aimed for students, scientists and professionals working in the field of agriculture and food technology.

"Micro-Raman Spectroscopy introduces readers to the theory and application of Raman microscopy. Raman microscopy is used to study the chemical signature of samples with little preparation in a non-destructive manner. An easy to use technique with ever increasing technological advances, Micro-Raman has significant application for researchers in the fields of materials science, medicine, pharmaceuticals, and chemistry."--

Recent advancements in imaging techniques and image analysis has broadened the horizons for their applications in various domains. Image analysis has become an influential technique in medical image analysis, optical character recognition, geology, remote sensing, and more. However, analysis of images under constrained and unconstrained environments require efficient representation of the data and complex models for accurate interpretation and classification of data. Deep learning methods, with their hierarchical/multilayered architecture, allow the systems to learn complex mathematical models to provide improved performance in the required task. The Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments provides a critical examination of the latest advancements, developments, methods, systems, futuristic approaches, and algorithms for image analysis and addresses its challenges. Highlighting concepts, methods, and tools including convolutional neural networks, edge enhancement, image segmentation, machine learning, and image processing, the book is an essential and comprehensive reference work for engineers, academicians, researchers, and students.

Biophotonics and Biosensing: From Fundamental Research to Clinical Trials Through Advances of Signal and Image Processing brings together the knowledge of the basic principles of the field of light-biological tissue interaction, detection methods, data processing techniques, and research, diagnostic and clinical applications. It is suitable for new entrants, while also highlighting the latest developments for experts in the field. This volume includes perspectives by leading experts from the biophotonics, biomedical engineering, and data science communities. The reader will receive a basic grounding in the key theoretical principles and practical components of biophotonics and biosensing. Working principles of devices used in spectroscopy, microscopy, and optical sensing are presented along with their application domains. The reader will learn about existing microscopy-based techniques used in biomedical applications for diagnosis and get to know different signal processing algorithms as used in biophotonics. Finally, through concrete examples, including sample preparation and measurement approaches, see how the field has developed thanks to the integration of biophotonics and optical biosensing with signal processing. - Introduces key principles of light-biological tissue interactions and biosensing - Discusses how the most promising optical diagnostic methods can exploit contemporary signal and image processing algorithms and data analytics - Includes examples of clinical studies with detailed descriptions of their implementation, along with practical guidance