Multi-modal Image Processing based on Coupled Dictionary Learning

In real-world scenarios, many data processing problems often involve heterogeneous images associated with different imaging modalities. Since these multimodal images originate from the same phenomenon, it is realistic to assume that they share common attributes or characteristics. In this paper, we propose a multi-modal image processing framework based on coupled dictionary learning to capture similarities and disparities between different image modalities. In particular, our framework can capture favorable structure similarities across different image modalities such as edges, corners, and other elementary primitives in a learned sparse transform domain, instead of the original pixel domain, that can be used to improve a number of image processing tasks such as denoising, inpainting, or super-resolution. Practical experiments demonstrate that incorporating multimodal information using our framework brings notable benefits.Comment: SPAWC 2018, 19th IEEE International Workshop On Signal Processing Advances In Wireless Communication

Multimodal Image Denoising based on Coupled Dictionary Learning

In this paper, we propose a new multimodal image denoising approach to attenuate white Gaussian additive noise in a given image modality under the aid of a guidance image modality. The proposed coupled image denoising approach consists of two stages: coupled sparse coding and reconstruction. The first stage performs joint sparse transform for multimodal images with respect to a group of learned coupled dictionaries, followed by a shrinkage operation on the sparse representations. Then, in the second stage, the shrunken representations, together with coupled dictionaries, contribute to the reconstruction of the denoised image via an inverse transform. The proposed denoising scheme demonstrates the capability to capture both the common and distinct features of different data modalities. This capability makes our approach more robust to inconsistencies between the guidance and the target images, thereby overcoming drawbacks such as the texture copying artifacts. Experiments on real multimodal images demonstrate that the proposed approach is able to better employ guidance information to bring notable benefits in the image denoising task with respect to the state-of-the-art.Comment: 2018 IEEE International Conference on Image Processing (ICIP). arXiv admin note: text overlap with arXiv:1806.0988

Controlling the 2D magnetism of CrBr3_3 by van der Waals stacking engineering

The manipulation of two-dimensional (2D) magnetic order is of significant importance to facilitate future 2D magnets for low-power and high-speed spintronic devices. Van der Waals stacking engineering makes promises for controllable magnetism via interlayer magnetic coupling. However, directly examining the stacking order changes accompanying magnetic order transitions at the atomic scale and preparing device-ready 2D magnets with controllable magnetic orders remain elusive. Here, we demonstrate effective control of interlayer stacking in exfoliated CrBr$_3$ via thermally assisted strain engineering. The stable interlayer ferromagnetic (FM), antiferromagnetic (AFM), and FM-AFM coexistent ground states confirmed by the magnetic circular dichroism measurements are realized. Combined with the first-principles calculations, the atomically-resolved imaging technique reveals the correlation between magnetic order and interlay stacking order in the CrBr$_3$ flakes unambiguously. A tunable exchange bias effect is obtained in the mixed phase of FM and AFM states. This work will introduce new magnetic properties by controlling the stacking order, and sequence of 2D magnets, providing ample opportunities for their application in spintronic devices.Comment: 7 pages, 4 figure

Multi-modal Image Processing via Joint Sparse Representations induced by Coupled Dictionaries

Real-world image processing tasks often involve various image modalities captured by different sensors. However, given that different sensors exhibit different characteristics, such multi-modal images are typically acquired with different resolutions, different blurring kernels, or even noise levels. In view of the fact that images associated with the same scene share some attributes, such as edges, textures or other primitives, it is natural to ask whether one can improve standard image processing tasks by leveraging the availability of multimodal images. This thesis introduces a sparsity-based machine learning framework along with algorithms to address such multimodal image processing problems. In particular, the thesis introduces a new coupled dictionary learning framework that is able to capture complex relationships and disparities between different image types in a learned sparse-representation domain in lieu of the original image domain. The thesis then introduces representative applications of this framework in key multimodal image processing problems. First, the thesis considers multi-modal image super-resolution problems where one wishes to super-resolve a certain low-resolution image modality given the availability of another high-resolution image modality of the same scene. It develops both a coupled dictionary learning algorithm and a coupled super-resolution algorithm to address this task arising in [1,2]. Second, the thesis considers multi-modal image denoising problems where one wishes to denoise a certain noisy image modality given the availability of another less noisy image modality of the same scene. The thesis develops an online coupled dictionary learning algorithm and a coupled sparse denoising algorithm to address this task arising in [3,4]. Finally, the thesis considers emerging medical imaging applications where one wishes to perform multi-contrast MRI reconstruction, including guided reconstruction and joint reconstruction. We propose an iterative framework to implement coupled dictionary learning, coupled sparse denoising and k-space consistency to address this task arising in [5,6]. The proposed framework is capable of capturing complex dependencies, including both similarities and disparities among multi-modal data. This enables transferring appropriate guidance information to the target image without introducing noticeable texture-copying artifacts. Practical experiments on multi-modal images also demonstrate that the proposed framework contributes to significant performance improvement in various image processing tasks, such as multi-modal image super-resolution, denoising and multi-contrast MRI reconstruction

Performance Analysis of Peer-to-Peer Online Lending Platforms in China

In this paper we intend to check the performance of Peer-to-Peer online lending platforms in China. Different from commercial banks, Peer-to-Peer (P2P) platforms’ business process is divided into the market-expanding stage and the risk-managing stage. In the market-expanding stage, platforms are intended to help borrowers attain more money, and in the risk-managing stage, platforms try their best to ensure that the lenders’ money is repaid on time. Thus, with a sample of 66 leading big P2P platforms, and a novel two-stage slacks-based measure data envelopment analysis with non-cooperative game, the performance efficiency of each stage as well as the comprehensive efficiency are evaluated. The results show that the leading big platforms are good at managing the risk, although risk management is not the major concern of most P2P platforms in China. We also find that average performance efficiency of the platforms that are located in non-first tier cities is higher than that in first tier cities. This unexpected result indicates that development of the P2P industry may relieve the severe distortion of resource allocation and efficiency loss arising from unbalanced regional development. Then dividing the platforms into different groups according to different types of ownership, we verify that performance efficiency of the P2P platforms from the state-owned enterprise group is in a dominant position, and the robustness check indicates that the major advantage of the state-owned enterprise (SOE) group mainly lies in the risk management. We also make a further study to figure out the sources of inefficiency, finding that it mainly arises from the shortage of lenders, the lack of average borrowing balance, and the insufficient transparency of information disclosure. In the last section we conclude our research and propose some advice

An Investigation of the Wear Resistance of Knitting Machinery Needle

Comparison tests of needles made in abroad and domestic on chemical composition, microstruture, mechanical property and so on, the main influence factors of the wear resistance were analysed. Approaches of improving the wear resistance of needles are given,such as hot process,surface treatment,local strengthening. Combined with finite element analysis, the stress and the strain of the needle hook were analysed. Comparative results of both abroad and domestic show that the carbide in the microstructure and structural design of needles are regarded as the main influence factors of the wear resistance

Coupled dictionary learning for multimodal image super-resolution

Real-world data processing problems often involve multiple data modalities, e.g., panchromatic and multispectral images, positron emission tomography (PET) and magnetic resonance imaging (MRI) images. As these modalities capture information associated with the same phenomenon, they must necessarily be correlated, although the precise relation is rarely known. In this paper, we propose a coupled dictionary learning (CDL) framework to automatically learn these relations. In particular, we propose a new data model to characterize both similarities and discrepancies between multimodal signals in terms of common and unique sparse representations with respect to a group of coupled dictionaries. However, learning these coupled dictionaries involves solving a highly non-convex structural dictionary learning problem. To address this problem, we design a coupled dictionary learning algorithm, referred to sequential recursive optimization (SRO) algorithm, to sequentially learn these dictionaries in a recursive manner. By capitalizing on our model and algorithm, we conceive a CDL based multimodal image super-resolution (SR) approach. Practical multispectral image SR experiments demonstrate that our SR approach outperforms the bicubic interpolation and the state-of-the-art dictionary learning based image SR approach, with Peak-SNR (PSNR) gains of up to 8.2 dB and 5.1 dB, respectively

Measurement matrix design for compressive sensing with side information at the encoder

We study the problem of measurement matrix design for Compressive Sensing (CS) when the encoder has access to side information, a signal analogous to the signal of interest. In particular, we propose to incorporate this extra information into the signal acquisition stage via a new design for the measurement matrix. The goal is to reduce the number of encoding measurements, while still allowing perfect signal reconstruction at the decoder. Then, the reconstruction performance of the resulting CS system is analysed in detail assuming the decoder reconstructs the original signal via Basis Pursuit. Finally, Gaussian width tools are exploited to establish a tight theoretical bound for the number of required measurements. Extensive numerical experiments not only validate our approach, but also demonstrate that our design requires fewer measurements for successful signal reconstruction compared with alternative designs, such as an i.i.d. Gaussian matrix