SPATIAL RESOLUTION ENHANCEMENT OF OVERSAMPLED IMAGES USING REGRESSION DECOMPOSITION AND SYNTHESIS

A new statistical model designed for regression analysis with a sparse design matrix is proposed. This new model utilizes the positions of the limited non-zero elements in the design matrix to decompose the regression model into sub-regression models. Statistical inferences are further made on the values of these limited non-zero elements to provide a reference for synthesizing these sub-regression models. With this concept of the regression decomposition and synthesis, the information on the structure of the design matrix can be incorporated into the regression analysis to provide a more reliable estimation. The proposed model is then applied to resolve the spatial resolution enhancement problem for spatially oversampled images. To systematically evaluate the performance of the proposed model in enhancing the spatial resolution, the proposed approach is applied to the oversampled images that are reproduced via random field simulations. These application results based on different generated scenarios then conclude the effectiveness and the feasibility of the proposed approach in enhancing the spatial resolution of spatially oversampled images.

Critical Evaluation of Spectral Resolution Enhancement Methods for Raman Hyperspectra

Overlapping peaks in Raman spectra complicate the presentation, interpretation, and analyses of complex samples. This is particularly problematic for methods dependent on sparsity such as multivariate curve resolution and other spectral demixing as well as for two-dimensional correlation spectroscopy (2D-COS), multisource correlation analysis, and principal component analysis. Though software-based resolution enhancement methods can be used to counter such problems, their performances often differ, thereby rendering some more suitable than others for specific tasks. Furthermore, there is a need for automated methods to apply to large numbers of varied hyperspectral data sets containing multiple overlapping peaks, and thus methods ideally suitable for diverse tasks. To investigate these issues, we implemented three novel resolution enhancement methods based on pseudospectra, over-deconvolution, and peak fitting to evaluate them along with three extant methods: node narrowing, blind deconvolution, and the general-purpose peak fitting program Fityk. We first applied the methods to varied synthetic spectra, each consisting of nine overlapping Voigt profile peaks. Improved spectral resolution was evaluated based on several criteria including the separation of overlapping peaks and the preservation of true peak intensities in resolution-enhanced spectra. We then investigated the efficacy of these methods to improve the resolution of measured Raman spectra. High resolution spectra of glucose acquired with a narrow spectrometer slit were compared to ones using a wide slit that degraded the spectral resolution. We also determined the effects of the different resolution enhancement methods on 2D-COS and on chemical contrast image generation from mammalian cell spectra. We conclude with a discussion of the particular benefits, drawbacks, and potential of these methods. Our efforts provided insight into the need for effective resolution enhancement approaches, the feasibility of these methods for automation, the nature of the problems currently limiting their use, and in particular those aspects that need improvement.

Deep Learning on Synthesized Sensor Characteristics and Transmission Spectra Enabling MEMS-Based Spectroscopic Gas Analysis beyond the Fourier Transform Limit

Miniaturized Fourier transform infrared spectrometers serve emerging market needs in many applications such as gas analysis. The miniaturization comes at the cost of lower performance than bench-top instrumentation, especially for the spectral resolution. However, higher spectral resolution is needed for better identification of the composition of materials. This article presents a convolutional neural network (CNN) for 3X resolution enhancement of the measured infrared gas spectra using a Fourier transform infrared (FTIR) spectrometer beyond the transform limit. The proposed network extracts a set of high-dimensional features from the input spectra and constructs high-resolution outputs by nonlinear mapping. The network is trained using synthetic transmission spectra of complex gas mixtures and simulated sensor non-idealities such as baseline drifts and non-uniform signal-to-noise ratio. Ten gases that are relevant to the natural and bio gas industry are considered whose mixtures suffer from overlapped features in the mid-infrared spectral range of 2000–4000 cm−1. The network results are presented for both synthetic and experimentally measured spectra using both bench-top and miniaturized MEMS spectrometers, improving the resolution from 60 cm−1 to 20 cm−1 with a mean square error down to 2.4×10−3 in the transmission spectra. The technique supports selective spectral analysis based on miniaturized MEMS spectrometers.

An Adversarial Learning Approach for Super-Resolution Enhancement Based on AgCl@Ag Nanoparticles in Scanning Electron Microscopy Images

Scanning electron microscopy (SEM) plays a crucial role in the characterization of nanoparticles. Unfortunately, due to the limited resolution, existing imaging techniques are insufficient to display all detailed characteristics at the nanoscale. Hardware-oriented techniques are troubled with costs and material properties. Computational approaches often prefer blurry results or produce a less meaningful high-frequency noise. Therefore, we present a staged loss-driven neural networks model architecture to transform low-resolution SEM images into super-resolved ones. Our approach consists of two stages: first, residual channel attention network (RCAN) with mean absolute error (MAE) loss was used to get a better peak signal-to-noise ratio (PSNR). Then, discriminators with adversarial losses were activated to reconstruct high-frequency texture features. The quantitative and qualitative evaluation results indicate that compared with other advanced approaches, our model achieves satisfactory results. The experiment in AgCl@Ag for photocatalytic degradation confirms that our proposed method can bring realistic high-frequency structural detailed information rather than meaningless noise. With this approach, high-resolution SEM images can be acquired immediately without sample damage. Moreover, it provides an enhanced characterization method for further directing the preparation of nanoparticles.

An overview of structured illumination microscopy: recent advances and perspectives

Structured illumination microscopy (SIM) is one of the most significant widefield super-resolution optical imaging techniques. The conventional SIM utilizes a sinusoidal structured pattern to excite the fluorescent sample; which eventually down-modulates higher spatial frequency sample information within the diffraction-limited passband of the microscopy system and provides around two-fold resolution enhancement over diffraction limit after suitable computational post-processing. Here we provide an overview of the basic principle, image reconstruction, technical development of the SIM technique. Nonetheless, in order to push the SIM resolution further towards the extreme nanoscale dimensions, several different approaches are launched apart from the conventional SIM. Among the various SIM methods, some of the important techniques e.g. TIRF, non-linear, plasmonic, speckle SIM etc. are discussed elaborately. Moreover, we highlight different implementations of SIM in various other imaging modalities to enhance their imaging performances with augmented capabilities. Finally, some future outlooks are mentioned which might develop fruitfully and pave the way for new discoveries in near future.

Laser Illumination Adjustments for Signal-to-Noise Ratio and Spatial Resolution Enhancement in Static 2D Chemical Images of NbOx/IGZO/ITO/Glass Light-Addressable Potentiometric Sensors

In a previous study, a thin In-Ga-Zn-oxide light addressable potentiometric sensor (IGZO LAPS) was indicated to have the advantages of low interference from ambient light, a high photocurrent and transfer efficiency, and a low cost. However, illumination optimization to obtain two-dimensional (2D) chemical images with better spatial resolutions has not been fully investigated. The trigger current and AC-modulated frequency of a 405-nm laser used to illuminate the fabricated IGZO LAPS were modified to check the photocurrent of the sensing area and SU8–2005 masking area, obtaining spatial resolution-related functions for the first time. The trigger current of illumination was adjusted from 0.020 to 0.030 A to compromise between an acceptable photocurrent and the integrity of the SU8–2005 masking layer. The photocurrent (PC) and differential photocurrent (DPC) versus scanning length (SL) controlled by an X-Y stage were used to check the resolved critical dimensions (CDs). The difference between resolved CD and optically measured CD (e.g., delta CD) measured at an AC frequency of 500 Hz revealed overall smaller values, supporting precise measurement in 2D imaging. The signal-to-noise ratio (SNR) has an optimized range of 2.0 to 2.15 for a better resolution for step spacings of both 10 and 2 μm in the scanning procedure to construct static 2D images. Under illumination conditions with a trigger current of 0.025 A and at an AC frequency of 500 Hz, the spatial resolution can be reduced to 10 μm from the pattern width of 6 μm. This developed methodology provides a quantitative evaluation with further optimization in spatial resolution without an extra cost for applications requiring a high spatial resolution, such as single-cell activity.

Spatial resolution enhancement of rotational-radar subsurface datasets using combined processing method

Effective visualisation of railway tunnel subsurface features (e.g. voids, utilities) provides critical insight into structural health and underpins planning of essential targeted predictive maintenance. Subsurface visualisation here utilises a rotating ground penetrating radar antenna system for 360° point cloud data capture. This technology has been constructed by our industry partner Railview Ltd, and requires the development of complimentary signal processing algorithms to improve feature localisation. The main novelty of this work is extension of Shrestha and Arai’s Combined Processing Method (CPM) to 360° Ground Penetrating Radar (360GPR) datasets, for first-time application in the context of railway tunnel structural health inspection. Initial experimental acquisition of a sample rotational transect for CPM enhancement is achieved by scanning a test section of tunnel sidewall - featuring predefined target geometry - with the rotating antenna. Next, frequency data separately undergo Inverse Fast Fourier Transform (IFFT) and Multiple Signal Classification (MUSIC) processing to recover temporal responses. Numerical implementation steps are explicitly provided for both MUSIC and two associated spatial smoothing algorithms, addressing an identified information deficit in the field. Described IFFT amplitude is combined with high spatial resolution of MUSIC via the CPM relation. Finally, temporal responses are compared qualitatively and quantitatively, evidencing the significant enhancement capabilities of CPM.