•A comprehensive review of existing multi-focus image fusion methods is provided.•A comparative study for MFIF is conducted and the relevant resources are released.•The current challenges and future ...trends of multi-focus image fusion are discussed.
Multi-focus image fusion is an effective technique to extend the depth-of-field of optical lenses by creating an all-in-focus image from a set of partially focused images of the same scene. In the last few years, great progress has been achieved in this field along with the rapid development of image representation theories and approaches such as multi-scale geometric analysis, sparse representation, deep learning, etc. This survey paper first presents a comprehensive overview of existing multi-focus image fusion methods. To keep up with the latest development in this field, a new taxonomy is introduced to classify existing methods into four main categories: transform domain methods, spatial domain methods, methods combining transform domain and spatial domain, and deep learning methods. For each category, representative fusion methods are introduced and summarized. Then, a comparative study for 18 representative fusion methods is conducted based on 30 pairs of commonly-used multi-focus images and 8 popular objective fusion metrics. All the relevant resources including source images, objective metrics and fusion results are released online, aiming to provide a benchmark for the future study of multi-focus image fusion. Finally, several major challenges remained in the current research of this field are discussed and some future prospects are put forward.
•Introduces Convolutional neural networks (CNNs) into the field of image fusion.•Discusses the feasibility and superiority of CNNs used for image fusion.•Proposes a state-of-the-art CNN-based ...multi-focus image fusion method.•Exhibits the potential of CNNs for other-type image fusion issues.•Puts forward some suggestions on the future study of CNN-based image fusion.
As is well known, activity level measurement and fusion rule are two crucial factors in image fusion. For most existing fusion methods, either in spatial domain or in a transform domain like wavelet, the activity level measurement is essentially implemented by designing local filters to extract high-frequency details, and the calculated clarity information of different source images are then compared using some elaborately designed rules to obtain a clarity/focus map. Consequently, the focus map contains the integrated clarity information, which is of great significance to various image fusion issues, such as multi-focus image fusion, multi-modal image fusion, etc. However, in order to achieve a satisfactory fusion performance, these two tasks are usually difficult to finish. In this study, we address this problem with a deep learning approach, aiming to learn a direct mapping between source images and focus map. To this end, a deep convolutional neural network (CNN) trained by high-quality image patches and their blurred versions is adopted to encode the mapping. The main novelty of this idea is that the activity level measurement and fusion rule can be jointly generated through learning a CNN model, which overcomes the difficulty faced by the existing fusion methods. Based on the above idea, a new multi-focus image fusion method is primarily proposed in this paper. Experimental results demonstrate that the proposed method can obtain state-of-the-art fusion performance in terms of both visual quality and objective assessment. The computational speed of the proposed method using parallel computing is fast enough for practical usage. The potential of the learned CNN model for some other-type image fusion issues is also briefly exhibited in the experiments.
The 2011 March 11 Tohoku-oki great (Mw 9.1) earthquake ruptured the plate boundary megathrust fault offshore of northern Honshu with estimates of shallow slip of 50m and more near the trench. ...Non-uniform slip extended ~220km across the width and ~400km along strike of the subduction zone. Extensive data provided by regional networks of seismic and geodetic stations in Japan and global networks of broadband seismic stations, regional and global ocean bottom pressure sensors and sea level measurement stations, seafloor GPS/Acoustic displacement sites, repeated multi-channel reflection images, extensive coastal runup and inundation observations, and in situ sampling of the shallow fault zone materials and temperature perturbation, make the event the best-recorded and most extensively studied great earthquake to date. An effort is made here to identify the more robust attributes of the rupture as well as less well constrained, but likely features. Other issues involve the degree to which the rupture corresponded to geodetically-defined preceding slip-deficit regions, the influence of re-rupture of slip regions for large events in the past few centuries, and relationships of coseismic slip to precursory slow slip, foreshocks, aftershocks, afterslip, and relocking of the megathrust. Frictional properties associated with the slip heterogeneity and in situ measurements of frictional heating of the shallow fault zone support low stress during shallow sliding and near-total shear stress drop of ~10–30MPa in large-slip regions in the shallow megathrust. The roles of fault morphology, sediments, fluids, and dynamical processes in the rupture behavior continue to be examined; consensus has not yet been achieved. The possibility of secondary sources of tsunami excitation such as inelastic deformation of the sedimentary wedge or submarine slumping remains undemonstrated; dislocation models in an elastic continuum appear to sufficiently account for most mainshock observations, although afterslip and viscoelastic processes remain contested.
•The rupture process of the 2011 Tohoku Mw 9.1 earthquake is reviewed.•Seismic, geodetic, seafloor, tsunami, and drill hole information is considered.•Joint inversions of various data now provide consistent models for coseismic slip.•Rupture extended to the trench with 50m or more slip near 38.2°N.•The stress drop may have totally relaxed elastic shear stress in large slip areas.
Measuring river water level (stage) is key for a variety of applications including discharge estimation and flood prediction. Although a variety of in situ and noncontact methods are available, there ...is an urgent need for new and more cost‐efficient methods. Rapid technological progress has accelerated the use of noncontact methods and development of time‐of‐flight distance sensors. Among available techniques, the use of lidar for distance measurement is promising because of its low cost, high energy efficiency, and small measurement footprint. However, lidar has rarely been used to measure water levels. Here we test a near‐infrared (905 nm) lidar sensor to determine its suitability for stage measurements under a range of environmental conditions. Using different laboratory and field setups, we assess sensor performance as a function of measurement distance, surface roughness, air temperature, water turbidity, and measurement angle. Despite the low reflectivity of water for infrared radiation, we find that the tested sensor is able to take measurements under all tested conditions, up to an incidence angle of ∼40°. The accuracy of the sensor is within the technical specifications of the device and is characterized by a relative error of around 0.1%. We find a strong dependence of the accuracy on sensor temperature, which we attribute to suboptimal internal compensation of the electronics. The precision of the sensor decreases with increasing measured distance and increases with surface roughness of the water body. We did not find any significant impact of water turbidity on the measurements.
Key Points
We describe a new noncontact lidar prototype to measure river water level
The prototype can take measurements under a range of environmental conditions, to ∼35 m range, and to inclinations of ∼40°
Lidar has the potential to enrich the observational database in data‐scarce regions globally
Increasing coastal inundation risk in a warming climate will require accurate and reliable seasonal forecasts of sea level anomalies at fine spatial scales. In this study, we explore statistical ...downscaling of monthly hindcasts from six current seasonal prediction systems to provide a high‐resolution prediction of sea level anomalies along the North American coast, including at several tide gauge stations. This involves applying a seasonally invariant downscaling operator, constructing by linearly regressing high‐resolution (1/12°) ocean reanalysis data against its coarse‐grained (1°) counterpart, to each hindcast ensemble member for the period 1982–2011. The resulting high‐resolution coastal hindcasts have significantly more deterministic skill than the original hindcasts interpolated onto the high‐resolution grid. Most of this improvement occurs during summer and fall, without impacting the seasonality of skill noted in previous studies. Analysis of the downscaling operator reveals that it boosts skill by amplifying the most predictable patterns while damping the less predictable patterns.
Plain Language Summary
Currently, the large computer models that form the basis of seasonal climate prediction systems produce coastal sea level forecasts spaced about 100 km apart. This is too coarse to meet the needs of U.S. coastal ocean management and services, which are becoming increasingly important as sea levels rise in a warming climate. In this study, we explored a method to provide such information on much smaller spatial scales, which better correspond to local coastal sea level measurements by tide gauges. We developed an efficient way to generate monthly sea level predictions on distances as small as 10 km apart, by applying the observed statistical relationship between sea level variations on scales of 100–1,000 km and finer‐scale coastal ocean observations to the original coarser model predictions. By testing our approach on past forecasts (“hindcasts”) from existing climate forecast systems, we found that we could improve forecasts for different local regions along both the U.S. West and East Coasts.
Key Points
Sea level prediction from relatively coarse operational forecasts can be enhanced to finer coastal scales using statistical downscaling
Downscaling can be determined by multivariate linear regression trained from high‐resolution reanalysis and its coarse‐grained counterpart
This downscaling method significantly improves skill compared to bilinearly interpolated hindcasts at several U.S. tide gauge locations
The paper considers the melted material level determining problem in the recuperative furnace bath melter in technological process automated control system for the basalt mineral fiber production. To ...solve this problem, it is proposed to use a visual level measurement system based on the analysis and processing of the melt boundary image. A block diagram of the visual level measurement system developed by the authors is presented, and an algorithm for determining the melt level using a modified Sobel edge detection method is proposed.
Multi-focus image fusion technique is an important approach to obtain a composite image with all objects in focus. The key point of multi-focus image fusion is to develop an effective activity level ...measurement to evaluate the clarity of source images. This paper proposes a novel image fusion method for multi-focus images with dense scale invariant feature transform (SIFT). The main novelty of this work is that it shows the great potential of image local features such as the dense SIFT used for image fusion. Particularly, the local feature descriptor can not only be employed as the activity level measurement, but also be used to match the mis-registered pixels between multiple source images to improve the quality of the fused image. In our algorithm, via the sliding window technique, the dense SIFT descriptor is first used to measure the activity level of source image patches to obtain an initial decision map, and then the decision map is refined with feature matching and local focus measure comparison. Experimental results demonstrate that the proposed method can be competitive with or even outperform the state-of-the-art fusion methods in terms of both subjective visual perception and objective evaluation metrics.
This article presents an algorithm for registering the arrival of tsunami waves based on the operational data of sea level measurements. The algorithm was developed using the fuzzy mathematics ...approach and implies an expert assessment while the procedure of adjustment and tuning. Its adaptive capabilities allow to function in accordance with the current preceding the arrival of a tsunami wave. The presented algorithm tends to be a universal tool that can be used for detecting the restructuring of processes according to measurements of their characteristics in time.
Microwave detection based on resonant peak tracking is the basis of various types of microwave near-field sensors. In this paper, a microwave near-field liquid level sensor based on an array of spoof ...localized surface plasmons (SLSPs) is proposed, which can be used to measure the liquid level in a non-metallic container in real time. Two arrays of the same SLSPs are distributed on the two branches of the splitter/combiner microstrip sections and excited by it. When the horizontal liquid level rises, the corresponding resonant peaks are shifted. And when the liquid level is tilted, the symmetrical condition of the system will be broken, and the resonant peaks of the corresponding SLSPs will be split, two notches (i.e., frequency splitting) appear and the resonant frequency will be changed. By analyzing the signal, the current liquid level situation and liquid level tilt angle can be judged. Taking an acrylic container as an example, simulations and experiments show that the sensor can measure the tilt angle with an max accuracy of up to 1°. Compared with other types of liquid level sensors, this sensor can penetrate the container for measurement, and has the advantages of high accuracy, low cost, easy container integration, and can measure the liquid surface tilt angle.
Traditional aircraft fuel level sensors, represented by capacitive fuel level sensors, have inherent shortcomings, making it difficult to meet the increasing aviation needs in terms of intrinsic ...safety and electromagnetic interference resistance. Therefore, this article studies the design technology of fiber optic fuel level sensors. By analyzing various optical fiber liquid level measurement methods, the fiber Bragg grating method that best meets practical needs is selected, and a novel and reasonable sensor structure design is achieved. Specifically, in order to improve the measurement accuracy, a lever-type sensitivity enhancing structure based on flexure hinge is studied in this paper. Combined with the metal diaphragm, it can generate higher axial strain on the fiber grating under a given liquid pressure. In order to ensure the practicality of the sensor, this paper analyzes the interference factors that may cause the measurement error in the actual fuel tank. Correspondingly, this paper proposes a design of fixed distance double measuring points to compensate the influence of posture, uses the reference fiber grating method to compensate the influence of temperature, and designs a reasonable assembly and sealing scheme. To verify the rationality of the design, the sensor test platform is built and the sensor function is tested comprehensively. The results show that the sensor has good precision and can achieve expected compensation effect.
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•A sensitivity enhancing structure is studied to achieve high measurement precision.•The sensor compensation scheme is designed to avoid interference during measurement.•A compact and well-sealed sensor assembly design is proposed.•The sensor function is tested comprehensively and the results are analyzed in detail.
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