This book serves as an introduction to the flourishing field of super-resolution imaging. It is a compiled volume, with different authors for each of its 14 chapters. While not having a strong outline or textbook format, the chapters group into several sections.

Super-Resolution Imaging

Single-molecule biophysics has transformed our understanding of biology, but also of the physics of life. More exotic than simple soft matter, biomatter lives far from thermal equilibrium, covering multiple lengths from the nanoscale of single molecules to up to several orders of magnitude higher in cells, tissues and organisms. Biomolecules are often characterized by underlying instability: multiple metastable free energy states exist, separated by levels of just a few multiples of the thermal energy scale k B T, where k B is the Boltzmann constant and T absolute temperature, implying complex inter-conversion kinetics in the relatively hot, wet environment of active biological matter. A key benefit of single-molecule biophysics techniques is their ability to probe heterogeneity of free energy states across a molecular population, too challenging in general for conventional ensemble average approaches. Parallel developments in experimental and computational techniques have catalysed the birth of multiplexed, correlative techniques to tackle previously intractable biological questions. Experimentally, progress has been driven by improvements in sensitivity and speed of detectors, and the stability and efficiency of light sources, probes and microfluidics. We discuss the motivation and requirements for these recent experiments, including the underpinning mathematics. These methods are broadly divided into tools which detect molecules and those which manipulate them. For the former we discuss the progress of super-resolution microscopy, transformative for addressing many longstanding questions in the life sciences, and for the latter we include progress in 'force spectroscopy' techniques that mechanically perturb molecules. We also consider in silico progress of single-molecule computational physics, and how simulation and experimentation may be drawn together to give a more complete understanding. Increasingly, combinatorial techniques are now used, including correlative atomic force microscopy and fluorescence imaging, to probe questions closer to native physiological behaviour. We identify the trade-offs, limitations and applications of these techniques, and discuss exciting new directions.

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Single-molecule techniques in biophysics: a review of the progress in methods and applications.

Each year, millions of dollars are invested on road maintenance and reparation all over the world. In order to minimize costs, one of the main aspects is the early detection of those flaws. Different types of cracks require different types of repairs; therefore, not only a crack detection is required but a crack type classification. Also, the earlier the crack is detected, the cheaper the reparation is. Once the images are captured, several processes are applied in order to extract the main characteristics for emphasizing the cracks (logarithmic transformation, bilateral filter, Canny algorithm, and a morphological filter). After image preprocessing, a decision tree heuristic algorithm is applied to finally classify the image. This work obtained an average of 88% of success detecting cracks and an 80% of success detecting the type of the crack. It could be implemented in a vehicle traveling as fast as 130 kmh or 81 mph.

/pdf/efficient-pavement-crack-detection-and-classification-4mfsmh4b6p.pdf

Efficient pavement crack detection and classification

Non-Invasive foetal electrocardiography (NI-FECG) represents an alternative foetal monitoring technique to traditional Doppler ultrasound approaches, that is non-invasive and has the potential to provide additional clinical information. However, despite the significant advances in the field of adult ECG signal processing over the past decades, the analysis of NI-FECG remains challenging and largely unexplored. This is mainly due to the relatively low signal-to-noise ratio of the FECG compared to the maternal ECG, which overlaps in both time and frequency. This article is intended to be used by researchers as a practical guide to NI-FECG signal processing, in the context of the above issues. It reviews recent advances in NI-FECG research including: publicly available databases, NI-FECG extraction techniques for foetal heart rate evaluation and morphological analysis, NI-FECG simulators and the methodology and statistics for assessing the performance of the extraction algorithms. Reference to the most recent work is given, recent findings are highlighted in the form of intermediate summaries, references to open source code and publicly available databases are provided and promising directions for future research are motivated. In particular we emphasise the need and specifications for building a new open reference database of NI-FECG signals, and the need for new algorithms to be benchmarked on the same database, employing the same evaluation statistics. Finally we motivate the need for research in NI-FECG to address morphological analysis, since this represent one of the most promising avenues for this foetal monitoring modality.

https://iopscience.iop.org/article/10.1088/0967-3334/37/5/R1/pdf

A practical guide to non-invasive foetal electrocardiogram extraction and analysis.

Ubiquitous motion blur easily fails multi-frame super-resolution (MFSR). Our method proposed in this paper tackles this issue by optimally searching least blurred pixels in MFSR. An EM framework is proposed to guide residual blur estimation and high-resolution image reconstruction. To suppress noise, we employ a family of sparse penalties as natural image priors, along with an effective solver. Theoretical analysis is performed on how and when our method works. The relationship between estimation errors of motion blur and the quality of input images is discussed. Our method produces sharp and higher-resolution results given input of challenging low-resolution noisy and blurred sequences.

Handling Motion Blur inMulti-Frame Super-Resolution

https://hal-enpc.archives-ouvertes.fr/hal-01811748/document

A new denoising model for multi-frame super-resolution image reconstruction

In this paper, we present a new approach of multi-frame super-resolution (SR). The SR techniques strongly depend on the availability of accurate motion estimation. When the estimation of motion is not well established, as usually happens for non-parametric motion, annoying artifacts appear in the super-resolved image. Since SR problems suffer from the motion and blur estimations, new techniques are considered to improve the registration and restoration steps. The proposed method consists of a non-parametric image registration based on diffusion regularization and a nonlocal Laplace regularizer combined with a bilateral filter (BTV) in the reconstruction step to remove noise and motion outliers. The diffusion registration is employed to handle the small deformation between the unregistered images, while the combination of nonlocal Laplace and BTV is used to increase the robustness of the restoration step with respect to the blurring effect and to the noise. We also prove the existence of a solution to the well posed registration problem. Simulation results using different images show the effectiveness and robustness of our algorithm against noise and outliers compared to other existing methods.

A multi-frame super-resolution using diffusion registration and a nonlocal variational image restoration

In this paper, we consider the image super-resolution (SR) reconstitution problem. The main goal consists of obtaining a high-resolution (HR) image from a set of low-resolution (LR) ones. For that, we propose a novel approach based on a regularized criterion. The criterion is composed of the classical generalized total variation (TV) but adding a bilateral filter (BTV) regularizer. The main goal of our approach consists of the derivation and the use of an efficient combined deblurring and denoising stage that is applied on the high-resolution image. We demonstrate the existence of minimizers of the combined variational problem in the bounded variation space, and we propose a minimization algorithm. The numerical results obtained by our approach are compared with the classical robust super-resolution (RSR) algorithm and the SR with TV regularization. They confirm that the proposed combined approach allows to overcome efficiently the blurring effect while removing the noise.

/pdf/a-combined-total-variation-and-bilateral-filter-approach-for-2lbuzif8hl.pdf

A combined total variation and bilateral filter approach for image robust super resolution

https://hal-enpc.archives-ouvertes.fr/hal-01616743/document

Simultaneous deconvolution and denoising using a second order variational approach applied to image super resolution

The multi-frame super-resolution(SR) aims to recover a high-resolution (HR) image from a degraded low-resolution (LR) sequence. Since the SR problem is considered as an ill-posed one, the regularization techniques are then inevitable. However, the choice of the fidelity and regularization terms is not easy and plays a major role in the quality of the desired HR image. In this paper, we propose a new nonconvex data fitting term and a fractional total variation regularization term for image super-resolution. The proposed model differs from existing image variational SR models where the fidelity term is always derived from the L 1 or L 2 - norm, and the regularization term is based on a widely choice of convex and nonconvex functions. The use of the nonconvex data fitting term can efficiently reduce complex noises such as impulse noise while the fractional order regularization term preserves image features like edges and texture much better. Numerical experiments show that the proposed model can produce competitive results, visually and quantitatively, compared to some available variational SR models.

Amine Laghrib

Papers

A new denoising model for multi-frame super-resolution image reconstruction

A multi-frame super-resolution using diffusion registration and a nonlocal variational image restoration

A combined total variation and bilateral filter approach for image robust super resolution

Simultaneous deconvolution and denoising using a second order variational approach applied to image super resolution

A nonconvex fractional order variational model for multi-frame image super-resolution