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The urban environment has a great impact on the wellbeing of citizens and it is of great significance to understand how citizens perceive and evaluate places in a large scale urban region and to provide scientific evidence to support human-centered urban planning with a better urban environment. Existing studies for assessing urban perception have primarily relied on low efficiency methods, which also result in low evaluation accuracy. Furthermore, there lacks a sophisticated understanding on how to correlate the urban perception with the built environment and other socio-economic data, which limits their applications in supporting urban planning. In this study, a new data-enabled intelligence framework for evaluating human perceptions of urban space is proposed. Specifically, a novel classification-then-regression strategy based on a deep convolutional neural network and a random-forest algorithm is proposed. The proposed approach has been applied to evaluate the perceptions of Beijing and Chengdu against six perceptual criteria. Meanwhile, multi-source data were employed to investigate the associations between human perceptions and the indicators for the built environment and socio-economic data including visual elements, facility attributes and socio-economic indicators. Experimental results show that the proposed framework can effectively evaluate urban perceptions. The associations between urban perceptions and the visual elements, facility attributes and a socio-economic dimension have also been identified, which can provide substantial inputs to guide the urban planning for a better urban space.

In recent years, to save bandwidth and storage space, images are usually compressed to reduce data volume, which leads to the loss of image details and affects the super-resolution (SR) performance. SR of compressed images is a key technique for addressing this problem. We propose the compressed-image super-resolution using enhanced attention network (CISREAN). First, the task is divided into two subtasks: image decompression and SR. Each subprocess introduces an enhanced residual module (ERM) with an attention mechanism. The ERM consists of several wide-activation residual blocks (WARBs) and an attention unit called the cascading residual attention (CRA) block. A WARB achieves better reconstruction than traditional image processing with the same computational complexity, and the CRA extracts more useful information in feature mapping even with fewer channels. This makes the ERM light and effective. Next, initial features are selected for wider inception and less blocking by overlapping extractions from the compressed image during decompression by convolutional layers. After completing both subprocesses, an end-to-end network is trained; it reduces compression artifacts, performing SR simultaneously. Extensive experiments on JPEG images with different quality factors show that CISREAN provides state-of-the-art performance based on objective metrics and subjective visual quality.

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.

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)