Large-scale Intensity Variations Research Articles

Adaptive zero-learning medical image fusion

Medical fusion image can integrate complementary information from multi-modal medical images, so that more comprehensive and accurate image results could be obtained. This paper proposes a new two-scale zero-learning medical images fusion method combined with pre-trained Res2Net and an adaptive guided filter. Firstly, this method utilizes the guided filter to decompose a medical image into a base layer representing large-scale intensity variations, and a detail layer containing small scale changes. Considering the influence of the parameters of the guided filter on the fusion images and the time-consuming on parameter selection, an adaptive guided filter based on the multi-modal medical image features is proposed. Then, detail layers are fused by an elementwise-sum strategy for retaining more detail information of source images. And the base layers are to fused with deep feature maps extracted from a pre-trained neural network Res2Net. Finally, the fused detail and basic layer will be reconstructed to obtain medical fusion image. The proposed method is demonstrated its superiority by ablation studies, and compared with 7 typical and advanced image fusion methods in visual effect and evaluation index. The experimental results show that the proposed image fusion method outperforms other methods in retaining effective detailed information and image clarity.

A New Image Fusion Method for ERT-EMT Dual-Modality System Used in Gas–Liquid–Solid Three-Phase Fluidized Bed

The electrical resistance tomography and electromagnetic tomography (ERT-EMT) dual-modality system can be used to reconstruct the image of cross-sectional three-phase distribution for gas-liquid-solid fluidized bed by fusing the reconstructed images of ERT and EMT. However, due to the small size of solid particles, the complexity of fluidization process, and the inconsistency of media distribution recognized by ERT and EMT, the existing fusion algorithms of dual-modality electrical tomography, which either divide the media distribution into binary distribution, or use the consistency of the media distribution to be recognized to improve the image quality, are not applicable. Aiming at the problem, a new image fusion method based on guided image filtering (GIF) and image statistics is proposed in this paper. Firstly, the GIF method is used to decompose the images of ERT and EMT into base layers containing large scale variations in intensity and detail layers with small scale details. Then, the weights corresponding to the base layers and detail layers are calculated with an image statistics method and GIF method, respectively. Through the process, the useful information in the reconstructed images of ERT and EMT are fully utilized. The simulation and experimental results demonstrate that the proposed fusion method can accurately reconstruct the gas-liquid-solid distribution.

Kanzelh\xf6he Observatory: Instruments, Data Processing and Data Products

Kanzelhöhe Observatory for Solar and Environmental Research (KSO) of the University of Graz (Austria) is in continuous operation since its foundation in 1943. Since the beginning, its main task was the regular observation of the Sun in full disc. In this long time span covering almost seven solar cycles, a substantial amount of data was collected, which is made available online. In this article we describe the separate processing steps from data acquisition to high level products for different observing wavelengths. First of all we present in detail the quality classification, which is important for further processing of the raw images. We show how we construct centre-to-limb variation (CLV) profiles and how we remove large scale intensity variations produced by the telescope optics in order to get images with uniform intensity and contrast. Another important point is an overview of the different data products from raw images to high contrast images with heliographic grids overlaid. As the data products are accessible via different sources, we also present how to get information about the availability and how to obtain these data. Finally, in an appendix, we describe in detail the information in the FITS headers, the file naming and the data hierarchy.

A new patch-based two-scale decomposition for infrared and visible image fusion

Typically, in existing two-scale fusion method, source images are first processed by a filter to obtain two layers, including a base layer and a detail layer. Second, a specific fusion strategy is applied to fuse these layers. Herein, the fusion of the base layer influences the contrast and clearness of final fusion result because the base layer may contain large scale variations in intensity. However, due to the inherent limitation of the base layer based on the filtering decomposition, the contrast of fusion result is easily reduced, or even the detail of the source images, in the fusion result, may be blurred. In this paper, a new two-scale fusion approach is proposed. In the process of two-scale decomposition, a new patch-based low-rank representation is proposed to obtain the two layers of the source images. In the proposed decomposition approach, the expression of the base layer is converted from a single filtering decomposition to a transform domain. It consists of a dictionary and corresponding coefficients. In the process of fusion, we design a global statistical fusion rule based on the coefficient value to achieve the base layer fusion, which improves the contrast of the final fusion result. For detail layer fusion, the L1-norm strategy is chosen to integrate small scale features contained in the detail layer into the final fusion result, which can contain clear detail. Experimental results illustrate that the proposed framework outperforms traditional approaches on the public database.

A full-disk image standardization of the chromosphere observation at Huairou Solar Observing Station

Observations of local features in the solar chromosphere began in 1992 at Huairou Solar Observing Station, while the full-disk chromosphere observations were carried out since 2000. In order to facilitate researchers to use full-disk chromosphere observation, algorithms have been developed to standardize the full-disk images. The algorithms include the determination of the center of the image and size standardization, geometric correction and intensity normalization. The solar limb of each image is determined from a histogram analysis of its intensity distribution. The center and radius are then calculated and the image is corrected for geometric distortions. Images are re-scaled to have a fixed radius of 500 pixels and centered within the 1024 × 1024 frame. Finally, large-scale variations in intensity, such as limb-darkening, are removed using a median filter. This paper provides a detailed description of these algorithms, and a summary of the properties of these chromospheric full-disk observations to be used for further scientific investigations.

Multi-focus image fusion with alternating guided filtering

Due to depth-of-field limitations, existing optical sensors cannot usually obtain a focused image that contains all of the objects in the same scene at different distances. These partially focused images cannot effectively represent the scene and may potentially hinder a complete and accurate understanding of the scene, or even the performance of further computer processes. Multi-focus image fusion can address this problem by fusing multiple images with different focus settings from the same scene into one focused image. This paper proposes a novel multi-focus image fusion method with alternating guided filtering (AGF). First, the source images are decomposed by AGF into base layers containing large-scale variations in intensity and detail layers containing small-scale details. Second, the base and detail layers are fused with saliency maps, which have been filtered using the different layers gradient features. The final fused image is obtained through a recombination of the fused base and detail layers. The experimental results demonstrate that, in terms of visual and quantitative evaluations, the proposed method is capable of representing source images well and demonstrates a significant improvement in fusion quality in comparison with other existing fusion methods.

Detail Enhancement for High Dynamic Range Images Rendering Based on Multi-Level Decomposition

Various tone reproduction operators have been proposed to display high dynamic range images on low dynamic range (LDR) devices. Many recent computational photography techniques decompose an image into a piecewise smooth base layer, containing large scale variations in intensity, and a residual detail layer capturing the smaller scale details in the image. In these techniques, its important to control the scale of the extracted details and it is often needed to manipulate details in order to avoid the appearance of visual artifacts. In this paper, a new method is proposed to preserve details for high dynamic range images tone reproduction using multi-level image decomposition. We show that current base-detail decomposition techniques, based on the bilateral filter, are limited in their ability to extract detail at arbitrary scales. Thus, we achieve detail enhancement by applying the bilateral filter iteratively, and this process is called multi-level decomposition. By minimizing the proposed energy function, we can choose the proper decomposition level. Simulation results demonstrate that the proposed method can acquire better visual quality in detail enhancement while make the base smoothness.

Computational geometry-based scale space for edge-preserving multiscale image decomposition

Recently many computational photography applications need to decompose an image into a piecewise smooth base layer, containing large-scale variations in intensity, and a residual detail layer capturing the smaller-scale details in the image. In these applications, the image decomposition method requires multiscale ability to avoid visual artifacts. In this paper, we propose a new model of image decomposition that has the properties of edge-preserving and multiscale ability. Inspired by techniques in computational geometry and morphological image analysis, we use the α -scale space of the input image to extract information about oscillations. We define detail as oscillations between upper and lower envelope of the input image. Building on the key observation that the spatial scale of oscillations is characterized by the α value, we develop an algorithm for decomposing images into multiple scales of superposed oscillations. Compared with traditional image decomposition methods, our method has three advantages as follows: (1) precisely controls scale parameter; (2) preserves edge while decomposing; and (3) decouples noise layer from noisy image. Finally, we compare our results with current existing edge-preserving image decomposition algorithms and demonstrate applications.

Image Fusion With Guided Filtering

A fast and effective image fusion method is proposed for creating a highly informative fused image through merging multiple images. The proposed method is based on a two-scale decomposition of an image into a base layer containing large scale variations in intensity, and a detail layer capturing small scale details. A novel guided filtering-based weighted average technique is proposed to make full use of spatial consistency for fusion of the base and detail layers. Experimental results demonstrate that the proposed method can obtain state-of-the-art performance for fusion of multispectral, multifocus, multimodal, and multiexposure images.

Edge-preserving decompositions for multi-scale tone and detail manipulation

Many recent computational photography techniques decompose an image into a piecewise smooth base layer, containing large scale variations in intensity, and a residual detail layer capturing the smaller scale details in the image. In many of these applications, it is important to control the spatial scale of the extracted details, and it is often desirable to manipulate details at multiple scales, while avoiding visual artifacts. In this paper we introduce a new way to construct edge-preserving multi-scale image decompositions. We show that current basedetail decomposition techniques, based on the bilateral filter, are limited in their ability to extract detail at arbitrary scales. Instead, we advocate the use of an alternative edge-preserving smoothing operator, based on the weighted least squares optimization framework, which is particularly well suited for progressive coarsening of images and for multi-scale detail extraction. After describing this operator, we show how to use it to construct edge-preserving multi-scale decompositions, and compare it to the bilateral filter, as well as to other schemes. Finally, we demonstrate the effectiveness of our edge-preserving decompositions in the context of LDR and HDR tone mapping, detail enhancement, and other applications.

The acceleration of cosmic rays in supernova remnants. II. Large-scale intensity variations

For pt.I see ibid., vol.12, p.1067-86, 1986. Bhat et al. have previously argued that the bulk of the cosmic rays below about 10 GeV are accelerated in supernova remnants. Here the authors search for support amongst results from gamma -ray astronomy on the large-scale distribution of cosmic rays. They conclude that there is support for this idea: the average intensity as a function of Galactocentric distance, the ratio between the average intensity in the second and third quadrants and the reduction in intensity in the interarm region centred on l approximately=65 degrees are all consistent with the SNR model.

Modulation of Jovian electron intensity in interplanetary space by corotating interaction regions

Large-scale intensity variations of Jovian electrons in the energy range from 3 to 6 MeV persisting for several days were observed with instrumentation on the Pioneer 10 and Pioneer 11 spacecraft out to several AU from Jupiter along the pre- and post-planetary encounter trajectories. The corotating interaction regions (CIRs), found by Smith and Wolfe (1976) on these missions to be approximately 25-day recurring regions of enhanced magnetic fields bounded by jumps in solar-wind velocity and frequently shocks, are shown to be impenetrable 'barriers' for the Jovian electrons propagating in the interplanetary medium. Thus, the principal electron-intensity variations are due to the modulating effects of CIRs and are not due to either variations in escape rate of electrons from the magnetosphere or interplanetary electron acceleration. The implications for electron observations at the orbit of earth are discussed.