30 research outputs found
Non-Local Robust Quaternion Matrix Completion for Large-Scale Color Images and Videos Inpainting
The image nonlocal self-similarity (NSS) prior refers to the fact that a
local patch often has many nonlocal similar patches to it across the image. In
this paper we apply such NSS prior to enhance the robust quaternion matrix
completion (QMC) method and significantly improve the inpainting performance. A
patch group based NSS prior learning scheme is proposed to learn explicit NSS
models from natural color images. The NSS-based QMC algorithm computes an
optimal low-rank approximation to the high-rank color image, resulting in high
PSNR and SSIM measures and particularly the better visual quality. A new joint
NSS-base QMC method is also presented to solve the color video inpainting
problem based quaternion tensor representation. The numerical experiments on
large-scale color images and videos indicate the advantages of NSS-based QMC
over the state-of-the-art methods.Comment: 22 pages, 10 figure
An inexact proximal majorization-minimization Algorithm for remote sensing image stripe noise removal
The stripe noise existing in remote sensing images badly degrades the visual
quality and restricts the precision of data analysis. Therefore, many
destriping models have been proposed in recent years. In contrast to these
existing models, in this paper, we propose a nonconvex model with a DC function
(i.e., the difference of convex functions) structure to remove the strip noise.
To solve this model, we make use of the DC structure and apply an inexact
proximal majorization-minimization algorithm with each inner subproblem solved
by the alternating direction method of multipliers. It deserves mentioning that
we design an implementable stopping criterion for the inner subproblem, while
the convergence can still be guaranteed. Numerical experiments demonstrate the
superiority of the proposed model and algorithm.Comment: 19 pages, 3 figure
H2TF for Hyperspectral Image Denoising: Where Hierarchical Nonlinear Transform Meets Hierarchical Matrix Factorization
Recently, tensor singular value decomposition (t-SVD) has emerged as a
promising tool for hyperspectral image (HSI) processing. In the t-SVD, there
are two key building blocks: (i) the low-rank enhanced transform and (ii) the
accompanying low-rank characterization of transformed frontal slices. Previous
t-SVD methods mainly focus on the developments of (i), while neglecting the
other important aspect, i.e., the exact characterization of transformed frontal
slices. In this letter, we exploit the potentiality in both building blocks by
leveraging the \underline{\bf H}ierarchical nonlinear transform and the
\underline{\bf H}ierarchical matrix factorization to establish a new
\underline{\bf T}ensor \underline{\bf F}actorization (termed as H2TF). Compared
to shallow counter partners, e.g., low-rank matrix factorization or its convex
surrogates, H2TF can better capture complex structures of transformed frontal
slices due to its hierarchical modeling abilities. We then suggest the
H2TF-based HSI denoising model and develop an alternating direction method of
multipliers-based algorithm to address the resultant model. Extensive
experiments validate the superiority of our method over state-of-the-art HSI
denoising methods
Unsupervised Deraining: Where Contrastive Learning Meets Self-similarity
Image deraining is a typical low-level image restoration task, which aims at
decomposing the rainy image into two distinguishable layers: the clean image
layer and the rain layer. Most of the existing learning-based deraining methods
are supervisedly trained on synthetic rainy-clean pairs. The domain gap between
the synthetic and real rains makes them less generalized to different real
rainy scenes. Moreover, the existing methods mainly utilize the property of the
two layers independently, while few of them have considered the mutually
exclusive relationship between the two layers. In this work, we propose a novel
non-local contrastive learning (NLCL) method for unsupervised image deraining.
Consequently, we not only utilize the intrinsic self-similarity property within
samples but also the mutually exclusive property between the two layers, so as
to better differ the rain layer from the clean image. Specifically, the
non-local self-similarity image layer patches as the positives are pulled
together and similar rain layer patches as the negatives are pushed away. Thus
the similar positive/negative samples that are close in the original space
benefit us to enrich more discriminative representation. Apart from the
self-similarity sampling strategy, we analyze how to choose an appropriate
feature encoder in NLCL. Extensive experiments on different real rainy datasets
demonstrate that the proposed method obtains state-of-the-art performance in
real deraining.Comment: 10 pages, 10 figures, accept to 2022CVP
Unsupervised Deraining: Where Asymmetric Contrastive Learning Meets Self-similarity
Most of the existing learning-based deraining methods are supervisedly
trained on synthetic rainy-clean pairs. The domain gap between the synthetic
and real rain makes them less generalized to complex real rainy scenes.
Moreover, the existing methods mainly utilize the property of the image or rain
layers independently, while few of them have considered their mutually
exclusive relationship. To solve above dilemma, we explore the intrinsic
intra-similarity within each layer and inter-exclusiveness between two layers
and propose an unsupervised non-local contrastive learning (NLCL) deraining
method. The non-local self-similarity image patches as the positives are
tightly pulled together, rain patches as the negatives are remarkably pushed
away, and vice versa. On one hand, the intrinsic self-similarity knowledge
within positive/negative samples of each layer benefits us to discover more
compact representation; on the other hand, the mutually exclusive property
between the two layers enriches the discriminative decomposition. Thus, the
internal self-similarity within each layer (similarity) and the external
exclusive relationship of the two layers (dissimilarity) serving as a generic
image prior jointly facilitate us to unsupervisedly differentiate the rain from
clean image. We further discover that the intrinsic dimension of the non-local
image patches is generally higher than that of the rain patches. This motivates
us to design an asymmetric contrastive loss to precisely model the compactness
discrepancy of the two layers for better discriminative decomposition. In
addition, considering that the existing real rain datasets are of low quality,
either small scale or downloaded from the internet, we collect a real
large-scale dataset under various rainy kinds of weather that contains
high-resolution rainy images.Comment: 16 pages, 15 figures. arXiv admin note: substantial text overlap with
arXiv:2203.1150
The characteristics of solid-phase substrate during the co-fermentation of lignite and straw
Co-fermentation of lignite and biomass has been considered as a new approach in achieving clean energy. Moreover, the study of the characteristics of solid phase in the synergistic degradation process is of great significance in revealing their synergistic relationship. Accordingly, in order to produce biogas, lignite, straw, and the mixture of the two were used as the substrates, the solid phase characteristics of which were analyzed before and after fermentation using modern analytical methods. The results revealed that the mixed fermentation of lignite and straw promoted the production of biomethane. Moreover, the ratios of C/O and C/H were found to be complementary in the co-fermentation process. Furthermore, while the relative content of C-C/C-H bonds was observed to be significantly decreased, the aromatics degree of lignite was weakened. Also, while the degree of branching increased, there found to be an increase in the content of cellulose amorphous zone, which, consequently, led to an increase in the crystallinity index of the wheat straw. Hence, the results provide a theoretical guidance for the efficient utilization of straw and lignite
A Human Activity Recognition Algorithm Based on Stacking Denoising Autoencoder and LightGBM
Recently, the demand for human activity recognition has become more and more urgent. It is widely used in indoor positioning, medical monitoring, safe driving, etc. Existing activity recognition approaches require either the location information of the sensors or the specific domain knowledge, which are expensive, intrusive, and inconvenient for pervasive implementation. In this paper, a human activity recognition algorithm based on SDAE (Stacking Denoising Autoencoder) and LightGBM (LGB) is proposed. The SDAE is adopted to sanitize the noise in raw sensor data and extract the most effective characteristic expression with unsupervised learning. The LGB reveals the inherent feature dependencies among categories for accurate human activity recognition. Extensive experiments are conducted on four datasets of distinct sensor combinations collected by different devices in three typical application scenarios, which are human moving modes, current static, and dynamic behaviors of users. The experimental results demonstrate that our proposed algorithm achieves an average accuracy of 95.99%, outperforming other comparative algorithms using XGBoost, CNN (Convolutional Neural Network), CNN + Statistical features, or single SDAE