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Remote sensing images have been widely used in many applications. However, the resolution of the obtained remote sensing images may not meet the increasing demands for some applications. In general, the sparse representation-based super-resolution (SR) method is one of the most popular methods to solve this issue. However, traditional sparse representation SR methods do not fully exploit the complementary constraints of images. Therefore, they cannot accurately reconstruct the unknown HR images. To address this issue, we propose a novel adaptive joint constraint (AJC) based on sparse representation for the single remote sensing image SR. First, we construct a nonlocal constraint by using the nonlocal self-similarity. Second, we propose a local structure filter according to the local gradient of the image and then construct a local constraint. Next, the nonlocal and local constraints are introduced into the sparse representation-based SR framework. Finally, the parameters of the joint constraint model are selected adaptively according to the level of image noise. We utilize the alternate iteration algorithm to tackle the minimization problem in AJC. Experimental results show that the proposed method achieves good SR performance in preserving image details and significantly improves the objective evaluation indices.

The parameter evaluation of reservoir rocks can help us to identify components and calculate the permeability and other parameters, and it plays an important role in the petroleum industry. Until now, computed tomography (CT) has remained an irreplaceable way to acquire the microstructure of reservoir rocks. During the evaluation and analysis, large samples and high-resolution images are required in order to obtain accurate results. Owing to the inherent limitations of CT, however, a large field of view results in low-resolution images, and high-resolution images entail a smaller field of view. Our method is a promising solution to these data collection limitations. In this study, a framework for sparse representation-based 3D volumetric super-resolution is proposed to enhance the resolution of 3D voxel images of reservoirs scanned with CT. A single reservoir structure and its downgraded model are divided into a large number of 3D cubes of voxel pairs and these cube pairs are used to calculate two overcomplete dictionaries and the sparse-representation coefficients in order to estimate the high frequency component. Future more, to better result, a new feature extract method with combine BM4D together with Laplacian filter are introduced. In addition, we conducted a visual evaluation of the method, and used the PSNR and FSIM to evaluate it qualitatively.

The reactor coolant pump is called the heart of the reactor. It is Safety Class-1 components [1]. Speed is an important parameter for reactor coolant pump operation. The structure and principle of the Class 1E speed sensor for reactor coolant pump in Nuclear Power Plant (NPP) is introduced. During the commissioning and operation of the reactor coolant pump, the Class 1E speed sensor has encountered many problems, including loss of signal and output error. To solve these problems, the authors analyze the waveform to identify the problem and then, according to the fault types, adjust the on-site installation mode or improve the signal processing unit.This paper presents the symptoms of the problem, the way to find the problem, and the final resolution, hoping to provide some solutions for peer workers to solve similar problems.

Super-resolution (SR), which aims at recovering a high-resolution (HR) image from single or sequential low-resolution (LR) images, is a widely used technology in image processing. In the field of image SR, convolutional neural networks (CNNs) have attracted increasing attention because of their high-quality performance. However, most CNN-based methods treat each channel-wise feature equally, which lacks discriminative learning ability across feature channels. Furthermore, many methods neglect to fully use information of each convolutional layer. To resolve these problems, we propose a remote sensing image SR method named dense channel attention network (DCAN). In our DCAN, a sequence of residual dense channel attention blocks (RDCABs) is cascaded with a densely connected structure. In each RDCAB, we make full use of the information from all convolution layers via densely connected convolutional layers. In addition, RDCABs utilize the channel attention mechanism to adaptively recalibrate channel-wise feature responses by explicitly modeling the interdependencies between the channels. In addition, our DCAN can make full use of the hierarchical features by densely connecting each RDCAB. Finally, to further improve the SR performance, the proposed DCAN is learned in both the pixel and wavelet domains, and a fusion layer is used to fuse the outputs of these two domains. Extensive quantitative and qualitative evaluations verify the superiority of our proposed method over several state-of-the-art methods.

Reconstructing a three-dimensional (3D) structure from a single two-dimensional training image (TI) is a challenging issue. Multiple-point statistics (MPS) is an effective method to solve this problem. However, in the traditional MPS method, errors occur while statistical features of reconstruction, such as porosity, connectivity, and structural properties, deviate from those of TI. Due to the MPS reconstruction mechanism that the voxel being reconstructed is dependent on the reconstructed voxel, it may cause error accumulation during simulations, which can easily lead to a significant difference between the real 3D structure and the reconstructed result. To reduce error accumulation and improve morphological similarity, an improved MPS method based on porosity matching is proposed. In the reconstruction, we search the matching pattern in the TI directly. Meanwhile, a multigrid approach is also applied to capture the large-scale structures of the TI. To demonstrate its superiority over the traditional MPS method, our method is tested on different sandstone samples from many aspects, including accuracy, stability, generalization, and flow characteristics. Experimental results show that the reconstruction results by the improved MPS method effectively match the CT sandstone samples in correlation functions, local porosity distribution, morphological parameters, and permeability.

We address space-time super-resolution (SR) problem in this paper. To efficiently explore the correlations within video sequences, a patch group (PG) model is proposed. The model is based on a novel 3D neighborhood system (NS) and embeds the spatial and temporal correlations. A patch group cuts (PGCuts) metric is then built on the model to provide a new prior for space-time SR reconstruction. To balance the prior strength for the whole video sequence, an adaptive scheme is also considered. We evaluate the proposed PGCuts prior on both synthesized and real video sequences. The results indicate that space-time SR method with the PGCuts prior outperforms other ones by retaining smooth edges and motion trajectories in videos, and by staying robust to noises as well. 3D neighborhood system accommodates more pixel correlations than traditional one.Patch group model based on similarity efficiently exploits space-time coherence.Smooth surfaces in patch group model results in smooth edges and motion trajectories.Edge strength based adaptive scheme effectively balances performance in the whole video.SR with patch group cuts produces better objective and subjective performance.

Video super-resolution (SR) is an inverse problem, which has gained much attention in these years. One of the core issues is how to better suppress noise and better preserve the edge. Multi-non-local regularization (MNLR) algorithm is efficient to reduce noise by utilizing the useful information from the correlated frames, but it might also cause some loss of high frequency at the same time. Multi-scale expected patch log likelihood (MSEPLL) and steering kernel regression (SKR) are both fine priors to describe the image characteristics, and could be used to recover details for low resolution images. However, their ability to suppress noise is slightly weaker than MNLR, which means they might preserve some details that MNLR method couldn't recover. Therefore, we consider using a residual framework to extract the useful information from the three reconstructed results. Simply, the images reconstructed by SKR and MSEPLL respectively minus the images reconstructed by MNLR method, and get two different residuals, then use different filters to process them to extract useful information. Finally, adding those processed residuals to the images reconstructed by MNLR method in an adaptive weighting way. The effectiveness of our proposed approach is demonstrated with experiments on video sequences.

Video super-resolution (SR) is an inverse problem, and with this method, we can reconstruct a high-resolution (HR) version of a low-resolution (LR) video sequence. Because regularization-based method can solve the pathological problem in super-resolution, so it is widely used. However, in many traditional regularization terms, only the intra-image correlation will be taken into consideration so that the redundancy between adjacent frames is not be utilized. In order to make full use of both inter-image correlation and intra-image correlation, we combine compensation-based TV (CTV) regularization term with multi-non-local low-rank (MNLR) regularization term in our algorithm. Moreover, we utilize a weight matrix to reduce the negative impacts which is caused by registration residuals in CTV, and the weight matrix is based on spatial information filtering and clustering. The experiments show that we can get better results than the compared methods by the proposed algorithm in visual quality and objective effective evaluation.

We report on a 1.645 μm single longitudinal mode Er:YAG laser that was resonantly pumped by a 1.532 μm fiber laser, using intracavity etalons to generate single longitudinal mode operation. We obtained 0.749 W single longitudinal mode output power at 1.645 μm from an Er:YAG laser with two intracavity etalons. The Msup2/sup-factors of the Er:YAG laser were 1.041 and 1.068 in the x and y directions, respectively.

Video-frame registration is a critical step in the video super-resolution process. However, many existing registration methods can handle only small local neighborhood movements or overall affine trans- formations. This limits the reconstruction of fast moving or significantly deforming objects. We propose a super-resolution registration method for video reconstruction. Considerable effort has been directed toward single-video and multivideo super-resolution methods. We aim to obtain a higher registration accuracy that maximally employs video frames to reconstruct the current frame, particularly for moving or deforming objects. To this end, we provide a content-based registration algorithm based on a propagation matching algorithm and the Lucas-Kanade method. The super-resolution step is implemented using robust iterative minimization. We compare our algorithm to others and demonstrate that our algorithm achieves high registration accuracy and more effectively reconstructs fast moving and significantly deforming objects. © 2014 SPIE and IS&T (DOI: 10.1117/1 .JEI.23.6.063018)