Quantifying the influence of Bessel beams on image quality in optical coherence tomography

Light scattered by turbid tissue is known to degrade optical coherence tomography (OCT) image contrast progressively with depth. Bessel beams have been proposed as an alternative to Gaussian beams to image deeper into turbid tissue. However, studies of turbid tissue comparing the image quality for different beam types are lacking. We present such a study, using numerically simulated beams and experimental OCT images formed by Bessel or Gaussian beams illuminating phantoms with optical properties spanning a range typical of soft tissue. We demonstrate that, for a given scattering parameter, the higher the scattering anisotropy the lower the OCT contrast, regardless of the beam type. When focusing both beams at the same depth in the sample, we show that, at focus and for equal input power and resolution, imaging with the Gaussian beam suffers less reduction of contrast. This suggests that, whilst Bessel beams offer extended depth of field in a single depth scan, for low numerical aperture (NA  0.95), superior contrast (by up to ~40%) may be obtained over an extended depth range by a Gaussian beam combined with dynamic focusing

Underwater Image Processing: State of the Art of Restoration and Image Enhancement Methods

The underwater image processing area has received considerable attention within the last decades, showing important achievements. In this paper we review some of the most recent methods that have been specifically developed for the underwater environment. These techniques are capable of extending the range of underwater imaging, improving image contrast and resolution. After considering the basic physics of the light propagation in the water medium, we focus on the different algorithms available in the literature. The conditions for which each of them have been originally developed are highlighted as well as the quality assessment methods used to evaluate their performance

Real-World Image Restoration Using Degradation Adaptive Transformer-Based Adversarial Network

Most existing learning-based image restoration methods heavily rely on paired degraded/non-degraded training datasets that are based on simplistic handcrafted degradation assumptions. These assumptions often involve a limited set of degradations, such as Gaussian blurs, noises, and bicubic downsampling. However, when these methods are applied to real-world images, there is a significant decrease in performance due to the discrepancy between synthetic and realistic degradation. Additionally, they lack the flexibility to adapt to unknown degradations in practical scenarios, which limits their generalizability to complex and unconstrained scenes. To address the absence of image pairs, recent studies have proposed Generative Adversarial Network (GAN)-based unpaired methods. Nevertheless, unpaired learning models based on convolution operations encounter challenges in capturing long-range pixel dependencies in real-world images. This limitation stems from their reliance on convolution operations, which offer local connectivity and translation equivariance but struggle to capture global dependencies due to their limited receptive field. To address these challenges, this dissertation proposed an innovative unpaired image restoration basic model along with an advanced model. The proposed basic model is the DA-CycleGAN model, which is based on the CycleGAN [1] neural network and specifically designed for blind real-world Single Image Super-Resolution (SISR). The DA-CycleGAN incorporates a degradation adaptive (DA) module to learn various real-world degradations (such as noise and blur patterns) in an unpaired manner, enabling strong flexible adaptation. Additionally, an advanced model called Trans-CycleGAN was designed, which integrated the Transformer architecture into CycleGAN to leverage its global connectivity. This combination allowed for image-to-image translation using CycleGAN [1] while enabling the Transformer to model global connectivity across long-range pixels. Extensive experiments conducted on realistic images demonstrate the superior performance of the proposed method in solving real-world image restoration problems, resulting in clearer and finer details. Overall, this dissertation presents a novel unpaired image restoration basic model and an advanced model that effectively address the limitations of existing approaches. The proposed approach achieves significant advancements in handling real-world degradations and modeling long-range pixel dependencies, thereby offering substantial improvements in image restoration tasks. Index Terms— Cross-domain translation, generative adversarial network, image restoration, super-resolution, transformer, unpaired training

A Non-Reference Evaluation of Underwater Image Enhancement Methods Using a New Underwater Image Dataset

The rise of vision-based environmental, marine, and oceanic exploration research highlights the need for supporting underwater image enhancement techniques to help mitigate water effects on images such as blurriness, low color contrast, and poor quality. This paper presents an evaluation of common underwater image enhancement techniques using a new underwater image dataset. The collected dataset is comprised of 100 images of aquatic plants taken at a shallow depth of up to three meters from three different locations in the Great Lake Superior, USA, via a Remotely Operated Vehicle (ROV) equipped with a high-definition RGB camera. In particular, we use our dataset to benchmark nine state-of-the-art image enhancement models at three different depths using a set of common non-reference image quality evaluation metrics. Then we provide a comparative analysis of the performance of the selected models at different depths and highlight the most prevalent ones. The obtained results show that the selected image enhancement models are capable of producing considerably better-quality images with some models performing better than others at certain depths

Exploring Himawari-8 geostationary observations for the advanced coastal monitoring of the Great Barrier Reef

Larissa developed an algorithm to enable water-quality assessment within the Great Barrier Reef (GBR) using weather satellite observations collected every 10 minutes. This unprecedented temporal resolution records the dynamic nature of water quality fluctuations for the entire GBR, with applications for improved monitoring and management

Underwater image restoration: super-resolution and deblurring via sparse representation and denoising by means of marine snow removal

Underwater imaging has been widely used as a tool in many fields, however, a major issue is the quality of the resulting images/videos. Due to the light's interaction with water and its constituents, the acquired underwater images/videos often suffer from a significant amount of scatter (blur, haze) and noise. In the light of these issues, this thesis considers problems of low-resolution, blurred and noisy underwater images and proposes several approaches to improve the quality of such images/video frames. Quantitative and qualitative experiments validate the success of proposed algorithms

Remote sensing and bio-geo-optical properties of turbid, productive inland waters: a case study of Lake Balaton

Algal blooms plague freshwaters across the globe, as increased nutrient loads lead to eutrophication of inland waters and the presence of potentially harmful cyanobacteria. In this context, remote sensing is a valuable approach to monitor water quality over broad temporal and spatial scales. However, there remain several challenges to the accurate retrieval of water quality parameters, and the research in this thesis investigates these in an optically complex lake (Lake Balaton, Hungary). This study found that bulk and specific inherent optical properties [(S)IOPs] showed significant spatial variability over the trophic gradient in Lake Balaton. The relationships between (S)IOPs and biogeochemical parameters differed from those reported in ocean and coastal waters due to the high proportion of particulate inorganic matter (PIM). Furthermore, wind-driven resuspension of mineral sediments attributed a high proportion of total attenuation to particulate scattering and increased the mean refractive index (n̅p) of the particle assemblage. Phytoplankton pigment concentrations [chlorophyll-a (Chl-a) and phycocyanin (PC)] were also accurately retrieved from a times series of satellite data over Lake Balaton using semi-analytical algorithms. Conincident (S)IOP data allowed for investigation of the errors within these algorithms, indicating overestimation of phytoplankton absorption [aph(665)] and underestimation of the Chl-a specific absorption coefficient [a*ph(665)]. Finally, Chl-a concentrations were accurately retrieved in a multiscale remote sensing study using the Normalized Difference Chlorophyll Index (NDCI), indicating hyperspectral data is not necessary to retrieve accurate pigment concentrations but does capture the subtle heterogeneity of phytoplankton spatial distribution. The results of this thesis provide a positive outlook for the future of inland water remote sensing, particularly in light of contemporary satellite instruments with continued or improved radiometric, spectral, spatial and temporal coverage. Furthermore, the value of coincident (S)IOP data is highlighted and contributes towards the improvement of remote sensing pigment retrieval in optically complex waters

Color Image Enhancement via Combine Homomorphic Ratio and Histogram Equalization Approaches: Using Underwater Images as Illustrative Examples

The histogram is one of the important characteristics of grayscale images, and the histogram equalization is effective method of image enhancement. When processing color images in models, such as the RGB model, the histogram equalization can be applied for each color component and, then, a new color image is composed from processed components. This is a traditional way of processing color images, which does not preserve the existent relation or correlation between colors at each pixel. In this work, a new model of color image enhancement is proposed, by preserving the ratios of colors at all pixels after processing the image. This model is described for the color histogram equalization (HE) and examples of application on color images are given. Our preliminary results show that the application of the model with the HE can be effectively used for enhancing color images, including underwater images. Intensive computer simulations show that for single underwater image enhancement, the presented method increases the image contrast and brightness and indicates a good natural appearance and relatively genuine color

Optical Satellite Remote Sensing of the Coastal Zone Environment — An Overview

Optical remote-sensing data are a powerful source of information for monitoring the coastal environment. Due to the high complexity of coastal environments, where different natural and anthropogenic phenomenon interact, the selection of the most appropriate sensor(s) is related to the applications required, and the different types of resolutions available (spatial, spectral, radiometric, and temporal) need to be considered. The development of specific techniques and tools based on the processing of optical satellite images makes possible the production of information useful for coastal environment management, without any destructive impacts. This chapter will highlight different subjects related to coastal environments: shoreline change detection, ocean color, water quality, river plumes, coral reef, alga bloom, bathymetry, wetland mapping, and coastal hazards/vulnerability. The main objective of this chapter is not an exhaustive description of the image processing methods/algorithms employed in coastal environmental studies, but focus in the range of applications available. Several limitations were identified. The major challenge still is to have remote-sensing techniques adopted as a routine tool in assessment of change in the coastal zone. Continuing research is required into the techniques employed for assessing change in the coastal environment

Beyond imaging with coherent anti-Stokes Raman scattering microscopy

La microscopie optique permet de visualiser des échantillons biologiques avec une bonne sensibilité et une résolution spatiale élevée tout en interférant peu avec les échantillons. La microscopie par diffusion Raman cohérente (CARS) est une technique de microscopie non linéaire basée sur l’effet Raman qui a comme avantage de fournir un mécanisme de contraste endogène sensible aux vibrations moléculaires. La microscopie CARS est maintenant une modalité d’imagerie reconnue, en particulier pour les expériences in vivo, car elle élimine la nécessité d’utiliser des agents de contraste exogènes, et donc les problèmes liés à leur distribution, spécificité et caractère invasif. Cependant, il existe encore plusieurs obstacles à l’adoption à grande échelle de la microscopie CARS en biologie et en médecine : le coût et la complexité des systèmes actuels, les difficultés d’utilisation et d’entretient, la rigidité du mécanisme de contraste, la vitesse de syntonisation limitée et le faible nombre de méthodes d’analyse d’image adaptées. Cette thèse de doctorat vise à aller au-delà de certaines des limites actuelles de l’imagerie CARS dans l’espoir que cela encourage son adoption par un public plus large. Tout d’abord, nous avons introduit un nouveau système d’imagerie spectrale CARS ayant une vitesse de syntonisation de longueur d’onde beaucoup plus rapide que les autres techniques similaires. Ce système est basé sur un laser à fibre picoseconde synchronisé qui est à la fois robuste et portable. Il peut accéder à des lignes de vibration Raman sur une plage importante (2700–2950 cm-1) à des taux allant jusqu’à 10 000 points spectrales par seconde. Il est parfaitement adapté pour l’acquisition d’images spectrales dans les tissus épais. En second lieu, nous avons proposé une nouvelle méthode d’analyse d’images pour l’évaluation de la structure de la myéline dans des images de sections longitudinales de moelle épinière. Nous avons introduit un indicateur quantitatif sensible à l’organisation de la myéline et démontré comment il pourrait être utilisé pour étudier certaines pathologies. Enfin, nous avons développé une méthode automatisé pour la segmentation d’axones myélinisés dans des images CARS de coupes transversales de tissu nerveux. Cette méthode a été utilisée pour extraire des informations morphologique des fibres nerveuses dans des images CARS de grande échelle.Optical-based microscopy techniques can sample biological specimens using many contrast mechanisms providing good sensitivity and high spatial resolution while minimally interfering with the samples. Coherent anti-Stokes Raman scattering (CARS) microscopy is a nonlinear microscopy technique based on the Raman effect. It shares common characteristics of other optical microscopy modalities with the added benefit of providing an endogenous contrast mechanism sensitive to molecular vibrations. CARS is now recognized as a great imaging modality, especially for in vivo experiments since it eliminates the need for exogenous contrast agents, and hence problems related to the delivery, specificity, and invasiveness of those markers. However, there are still several obstacles preventing the wide-scale adoption of CARS in biology and medicine: cost and complexity of current systems as well as difficulty to operate and maintain them, lack of flexibility of the contrast mechanism, low tuning speed and finally, poor accessibility to adapted image analysis methods. This doctoral thesis strives to move beyond some of the current limitations of CARS imaging in the hope that it might encourage a wider adoption of CARS as a microscopy technique. First, we introduced a new CARS spectral imaging system with vibrational tuning speed many orders of magnitude faster than other narrowband techniques. The system presented in this original contribution is based on a synchronized picosecond fibre laser that is both robust and portable. It can access Raman lines over a significant portion of the highwavenumber region (2700–2950 cm-1) at rates of up to 10,000 spectral points per second and is perfectly suitable for the acquisition of CARS spectral images in thick tissue. Secondly, we proposed a new image analysis method for the assessment of myelin health in images of longitudinal sections of spinal cord. We introduced a metric sensitive to the organization/disorganization of the myelin structure and showed how it could be used to study pathologies such as multiple sclerosis. Finally, we have developped a fully automated segmentation method specifically designed for CARS images of transverse cross sections of nerve tissue.We used our method to extract nerve fibre morphology information from large scale CARS images