Spatial Enhancement of AWiFS along Wider Swath using  NSCT

Image acquisition in a wider swath, cannot assess the best spatial resolution (SR) and temporal resolution (TR) simultaneously, due to inherent limitations of space borne sensors. But any of the information extraction from remote sensed (RS) images demands the above characteristics. As this is not possible onboard, suitable ground processing techniques need to be evolved to realise the requirements through advanced image processing techniques. The proposed work deals with processing of two onboard sensor data viz., Resourcesat-1 (RS1): LISS-III, which has medium swath combined with AWiFS, which has wider swath data to provide high spatial and temporal resolution at the same instant. LISS-III at 23m and 24 days, AWiFS at 56m and 5 days spatial and temporal revisits acquire the data at different swaths. In the process of acquisition at the same time, the 140km swath of LISS-III coincides at the exact centre line 740km swath of AWiFS. If the non-overlapping area of AWiFS has same features of earth’s surface as of LISS-III overlapping area, it then provides a way to increase the SR of AWiFS to SR of LISS-III in the same non-overlapping area. Using this knowledge, a novel processing technique Fast One Pair Learning and Prediction (FOPLP) is developed in which time is optimized against the existing methods. FOPLP improves the SR of LISS-III in non-overlapping area using technique Single Image Super Resolution (SISR) with Non Sub sampled Contourlet Transforms (NSCT) method and is applied on different sets of images. The proposed technique resulting into an image having TR of 5 days, 740km swath at SR of 23m. Results have shown the strength of the proposed method in terms of computation time and prediction accuracy assessment.

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Current understanding and applications of the cold sintering process

Frontiers of Chemical Science and Engineering ◽

10.1007/s11705-019-1832-1 ◽

2019 ◽

Vol 13 (4) ◽

pp. 654-664 ◽

Cited By ~ 3

Author(s):

Tong Yu ◽

Jiang Cheng ◽

Lu Li ◽

Benshuang Sun ◽

Xujin Bao ◽

...

Keyword(s):

Aqueous Solution ◽

Sintering Temperature ◽

Applied Pressure ◽

Processing Technique ◽

Rechargeable Batteries ◽

Processing Conditions ◽

Ceramic Processing ◽

Sintering Process ◽

Novel Processing ◽

Processing Techniques

Abstract In traditional ceramic processing techniques, high sintering temperature is necessary to achieve fully dense microstructures. But it can cause various problems including warpage, overfiring, element evaporation, and polymorphic transformation. To overcome these drawbacks, a novel processing technique called “cold sintering process (CSP)” has been explored by Randall et al. CSP enables densification of ceramics at ultra-low temperature (⩽300°C) with the assistance of transient aqueous solution and applied pressure. In CSP, the processing conditions including aqueous solution, pressure, temperature, and sintering duration play critical roles in the densification and properties of ceramics, which will be reviewed. The review will also include the applications of CSP in solid-state rechargeable batteries. Finally, the perspectives about CSP is proposed.

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Single Image Super-Resolution Based on Sparse Representation with Adaptive Dictionary Selection

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001416540069 ◽

2016 ◽

Vol 30 (07) ◽

pp. 1654006 ◽

Cited By ~ 4

Author(s):

Xin Li ◽

Jie Chen ◽

Ziguan Cui ◽

Minghu Wu ◽

Xiuchang Zhu

Keyword(s):

Sparse Representation ◽

Super Resolution ◽

Computation Time ◽

Resolution Image ◽

Image Patches ◽

Different Types ◽

Image Super Resolution ◽

Sr Reconstruction ◽

Single Image Super Resolution

Sparse representation theory has attracted much attention, and has been successfully used in image super-resolution (SR) reconstruction. However, it could only provide the local prior of image patches. Field of experts (FoE) is a way to develop the generic and expressive prior of the whole image. The algorithm proposed in this paper uses the FoE model as the global constraint of SR reconstruction problem to pre-process the low-resolution image. Since a single dictionary could not accurately represent different types of image patches, our algorithm classifies the sample patches composed of pre-processed image and high-resolution image, obtains the sub-dictionaries by training, and adaptively selects the most appropriate sub-dictionary for reconstruction according to the pyramid histogram of oriented gradients feature of image patches. Furthermore, in order to reduce the computational complexity, our algorithm makes use of edge detection, and only applies SR reconstruction based on sparse representation to the edge patches of the test image. Nonedge patches are directly replaced by the pre-processing results of FoE model. Experimental results show that our algorithm can effectively guarantee the quality of the reconstructed image, and reduce the computation time to a certain extent.

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Generating High Resolution Climate Change Projections through Single Image Super-Resolution: An Abridged Version

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/759 ◽

2018 ◽

Cited By ~ 10

Author(s):

Thomas Vandal ◽

Evan Kodra ◽

Sangram Ganguly ◽

Andrew Michaelis ◽

Ramakrishna Nemani ◽

...

Keyword(s):

Climate Change ◽

High Resolution ◽

Statistical Downscaling ◽

Power Plants ◽

Super Resolution ◽

Critical Systems ◽

Resolution Mapping ◽

Spatio Temporal ◽

Single Image Super Resolution ◽

Processing Techniques

The impacts of climate change are felt by most critical systems, such as infrastructure, ecological systems, and power-plants. However, contemporary Earth System Models (ESM) are run at spatial resolutions too coarse for assessing effects this localized. Local scale projections can be obtained using statistical downscaling, a technique which uses historical climate observations to learn a low-resolution to high-resolution mapping. The spatio-temporal nature of the climate system motivates the adaptation of super-resolution image processing techniques to statistical downscaling. In our work, we present DeepSD, a generalized stacked super resolution convolutional neural network (SRCNN) framework with multi-scale input channels for statistical downscaling of climate variables. A comparison of DeepSD to four state-of-the-art methods downscaling daily precipitation from 1 degree (~100km) to 1/8 degrees (~12.5km) over the Continental United States. Furthermore, a framework using the NASA Earth Exchange (NEX) platform is discussed for downscaling more than 20 ESM models with multiple emission scenarios.

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Deep fractal residual network for fast and accurate single image super resolution

Neurocomputing ◽

10.1016/j.neucom.2019.09.093 ◽

2020 ◽

Vol 398 ◽

pp. 389-398 ◽

Cited By ~ 1

Author(s):

Yanghao Zhou ◽

Jianfeng Dong ◽

Yubin Yang

Keyword(s):

Super Resolution ◽

Single Image ◽

Residual Network ◽

Image Super Resolution ◽

Single Image Super Resolution

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Real-Time Environment Monitoring Using a Lightweight Image Super-Resolution Network

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18115890 ◽

2021 ◽

Vol 18 (11) ◽

pp. 5890

Author(s):

Qiang Yu ◽

Feiqiang Liu ◽

Long Xiao ◽

Zitao Liu ◽

Xiaomin Yang

Keyword(s):

Deep Learning ◽

Real Time ◽

Super Resolution ◽

Model Complexity ◽

Practical Application ◽

Single Image ◽

Feature Maps ◽

Benchmark Datasets ◽

Image Super Resolution ◽

Single Image Super Resolution

Deep-learning (DL)-based methods are of growing importance in the field of single image super-resolution (SISR). The practical application of these DL-based models is a remaining problem due to the requirement of heavy computation and huge storage resources. The powerful feature maps of hidden layers in convolutional neural networks (CNN) help the model learn useful information. However, there exists redundancy among feature maps, which can be further exploited. To address these issues, this paper proposes a lightweight efficient feature generating network (EFGN) for SISR by constructing the efficient feature generating block (EFGB). Specifically, the EFGB can conduct plain operations on the original features to produce more feature maps with parameters slightly increasing. With the help of these extra feature maps, the network can extract more useful information from low resolution (LR) images to reconstruct the desired high resolution (HR) images. Experiments conducted on the benchmark datasets demonstrate that the proposed EFGN can outperform other deep-learning based methods in most cases and possess relatively lower model complexity. Additionally, the running time measurement indicates the feasibility of real-time monitoring.

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