Assessing the effect of noise-reduction to the intelligibility of low-pass filtered speech

Given the fact that most hearing-impaired listeners have low-frequency residual hearing, the present work assessed the effect of applying commonly-used singlechannel noise-reduction (NR) algorithms to improve the intelligibility of low-pass filtered speech, which simulates the effect of understanding speech with low-frequency residual hearing of hearing-impaired patients. In addition, this study was performed with Mandarin speech, which is characterized by its significant contribution of information present in (low-frequency dominated) vowels to speech intelligibility. Mandarin sentences were corrupted by steady-state speech-shaped noise and processed by four types (i.e., subspace, statistical-modeling, spectral-subtractive, and Wiener-filtering) of single-channel NR algorithms. The processed sentences were played to normal-hearing listeners for recognition. Experimental results showed that existing single-channel NR algorithms were unable to improve the intelligibility of low-pass filtered Mandarin sentences. Wiener-filtering had the least negative influence to the intelligibility of low-pass filtered speech among the four types of single-channel NR algorithms examined

一种基于模糊成像机理的QR码图像快速盲复原方法.

A fast blind restoration method of QR code images was proposed based on a blurred imaging mechanism. On the basis of the research on the centroid invariance of the blurred imaging diffuse light spots, the circular finder pattern is designed. When the image is blurred, the centroid of the pattern and the position of the QR code symbol can be quickly detected by methods such as connected components. Moreover, combined with step edge characteristics, gradient and intensity characteristics, edge detection technology, and optical imaging mechanism, the defocus radius of the blurred QR code image can be quickly and accurately estimated. Furthermore, the Wiener filter is applied to restore the QR code image quickly and effectively. Compared with the other algorithms, the proposed method has improved deblurring results in both structural similarity and peak signal-to-noise ratio, especially in the recovery speed. The average recovery time is 0.329 2 s. Experimental results show that this method can estimate the defocus radius with high accuracy and can quickly realize the blind restoration of QR code images. It has the advantages of rapidity and robustness, which are convenient for embedded hardware implementation and suitable for barcode identification-related industrial Internet of Things application scenarios

Cognitive fusion of thermal and visible imagery for effective detection and tracking of pedestrians in videos

BACKGROUND INTRODUCTION In this paper, we present an efficient framework to cognitively detect and track salient objects from videos. In general, colored visible image in red-green-blue (RGB) has better distinguishability in human visual perception, yet it suffers from the effect of illumination noise and shadows. On the contrary, the thermal image is less sensitive to these noise effects though its distinguishability varies according to environmental settings. To this end, cognitive fusion of these two modalities provides an effective solution to tackle this problem. METHODS First, a background model is extracted followed by two stage background-subtraction for foreground detection in visible and thermal images. To deal with cases of occlusion or overlap, knowledge based forward tracking and backward tracking are employed to identify separate objects even the foreground detection fails. RESULTS To evaluate the proposed method, a publicly available color-thermal benchmark dataset OTCBVS is employed here. For our foreground detection evaluation, objective and subjective analysis against several state-of-the-art methods have been done on our manually segmented ground truth. For our object tracking evaluation, comprehensive qualitative experiments have also been done on all video sequences. CONCLUSIONS Promising results have shown that the proposed fusion based approach can successfully detect and track multiple human objects in most scenes regardless of any light change or occlusion problem

C. elegans fatty acid two-hydroxylase regulates intestinal homeostasis by affecting heptadecenoic acid production

Background/Aims: The hydroxylation of fatty acids at the C-2 position is the first step of fatty acid α-oxidation and generates sphingolipids containing 2-hydroxy fatty acyl moieties. Fatty acid 2-hydroxylation is catalyzed by Fatty acid 2-hydroxylase (FA2H) enzyme. However, the precise roles of FA2H and fatty acid 2-hydroxylation in whole cell homeostasis still remain unclear. Methods: Here we utilize Caenorhabditis elegans as the model and systemically investigate the physiological functions of FATH-1/C25A1.5, the highly conserved worm homolog for mammalian FA2H enzyme. Immunostaining, dye-staining and translational fusion reporters were used to visualize FATH-1 protein and a variety of subcellular structures. The “click chemistry” method was employed to label 2-OH fatty acid in vivo. Global and tissue-specific RNAi knockdown experiments were performed to inactivate FATH-1 function. Lipid analysis of the fath-1 deficient mutants was achieved by mass spectrometry. Results: C. elegans FATH-1 is expressed at most developmental stages and in most tissues. Loss of fath-1 expression results in severe growth retardation and shortened lifespan. FATH-1 function is crucially required in the intestine but not the epidermis with stereospecificity. The “click chemistry” labeling technique showed that the FATH-1 metabolites are mainly enriched in membrane structures preferable to the apical side of the intestinal cells. At the subcellular level, we found that loss of fath-1 expression inhibits lipid droplets formation, as well as selectively disrupts peroxisomes and apical endosomes. Lipid analysis of the fath-1 deficient animals revealed a significant reduction in the content of heptadecenoic acid, while other major FAs remain unaffected. Feeding of exogenous heptadecenoic acid (C17: 1), but not oleic acid (C18: 1), rescues the global and subcellular defects of fath-1 knockdown worms. Conclusion: Our study revealed that FATH-1 and its catalytic products are highly specific in the context of chirality, C-chain length, spatial distribution, as well as the types of cellular organelles they affect. Such an unexpected degree of specificity for the synthesis and functions of hydroxylated FAs helps to regulate protein transport and fat metabolism, therefore maintaining the cellular homeostasis of the intestinal cells. These findings may help our understanding of FA2H functions across species, and offer potential therapeutical targets for treating FA2H-related diseases

Fast implementation of singular spectrum analysis for effective feature extraction in hyperspectral imaging

As a recent approach for time series analysis, singular spectrum analysis (SSA) has been successfully applied for feature extraction in hyperspectral imaging (HSI), leading to increased accuracy in pixel-based classification tasks. However, one of the main drawbacks of conventional SSA in HSI is the extremely high computational complexity, where each pixel requires individual and complete singular value decomposition (SVD) analyses. To address this issue, a fast implementation of SSA (F-SSA) is proposed for efficient feature extraction in HSI. Rather than applying pixel-based SVD as conventional SSA does, the fast implementation only needs one SVD applied to a representative pixel, i.e., either the median or the mean spectral vector of the HSI hypercube. The result of SVD is employed as a unique transform matrix for all the pixels within the hypercube. As demonstrated in experiments using two well-known publicly available data sets, almost identical results are produced by the fast implementation in terms of accuracy of data classification, using the support vector machine (SVM) classifier. However, the overall computational complexity has been significantly reduced

Multi-scale spatial fusion and regularization induced unsupervised auxiliary task CNN model for deep super-resolution of hyperspectral image.

Hyperspectral images (HSI) features rich spectral information in many narrow bands but at a cost of a relatively low spatial resolution. As such, various methods have been developed for enhancing the spatial resolution of the low-resolution HSI (Lr-HSI) by fusing it with high-resolution multispectral images (Hr-MSI). The difference in spectrum range and spatial dimensions between the Lr-HSI and Hr-SI have been fundamental but challenging for multispectral/hyperspectral (MS/HS) fusion. In this paper, a multi-scale spatial fusion and regularization induced auxiliary task (MSAT) based CNN model is proposed for deep super-resolution of HSI, where a Lr-HSI is fused with a Hr-MSI to reconstruct a high-resolution HSI (Hr-HSI) counterpart. The multi-scale fusion is used to efficiently address the discrepancy in spatial resolutions between two inputs. Based on the general assumption that the acquired Hr-MSI and the reconstructed Hr-HSI share similar underlying characteristics, the auxiliary task is proposed to learn a representation for improved generality of the model and reduced overfitting. Experimental results on three public datasets have validated the effectiveness of our approach in comparison with several state-of-the-art methods

Multiscale 2-D singular spectrum analysis and principal component analysis for spatial–spectral noise-robust feature extraction and classification of hyperspectral images.

In hyperspectral images (HSI), most feature extraction and data classification methods rely on corrected dataset, in which the noisy and water absorption bands are removed. This can result in not only extra working burden but also information loss from removed bands. To tackle these issues, in this article, we propose a novel spatial-spectral feature extraction framework, multiscale 2-D singular spectrum analysis (2-D-SSA) with principal component analysis (PCA) (2-D-MSSP), for noise-robust feature extraction and data classification of HSI. First, multiscale 2-D-SSA is applied to exploit the multiscale spatial features in each spectral band of HSI via extracting the varying trends within defined windows. Taking the extracted trend signals at each scale level as the input features, the PCA is employed to the spectral domain for dimensionality reduction and spatial-spectral feature extraction. The derived spatial-spectral features in each scale are separately classified and then fused at decision-level for efficacy. As our 2-D-MSSP method can extract features and simultaneously remove noise in both spatial and spectral domains, which ensures it to be noise-robust for classification of HSI, even the uncorrected dataset. Experiments on three publicly available datasets have fully validated the efficacy and robustness of the proposed approach, when benchmarked with 10 state-of-the-art classifiers, including six spatial-spectral methods and four deep learning classifiers. In addition, both quantitative and qualitative assessment has validated the efficacy of our approach in noise-robust classification of HSI even with limited training samples, especially in classifying uncorrected data without filtering noisy bands

Two-click based fast small object annotation in remote sensing images.

In the remote sensing field, detecting small objects is a pivotal task, yet achieving high performance in deep learning-based detectors heavily relies on extensive data annotation. The challenge intensifies as small objects in remote sensing imagery are typically densely distributed and numerous, leading to a substantial increase in the cost of creating large-scale annotated datasets. This elevated cost poses significant limitations on the application and advancement of small object detection. To address this issue, a Point-Based Annotation method (PBA) is proposed, which generates bounding boxes through graph-based segmentation. In this framework, user annotations categorize nodes into three distinct classes - positive, negative, and to-cut-facilitating a more intuitive and efficient annotation process. Utilizing the max-flow algorithm, our method seamlessly generates Oriented Bounding Boxes (OBBOX) from these classified nodes. The efficacy of PBA is underscored by our empirical findings. Notably, annotation efficiency is enhanced by at least 40%, a significant leap forward. Moreover, the Intersection over Union (IoU) metric of our OBBOX outperforms existing methods like "Segment Anything Model" by 10%. Finally, when applied in training, models annotated with PBA exhibit a 3% increase in the mean Average Precision (mAP) compared to those using traditional annotation methods. These results not only affirm the technical superiority of PBA but also its practical impact in advancing small object detection in remote sensing

Fast blind deblurring of QR code images based on adaptive scale control.

With the development of 5G technology, the short delay requirements of commercialization and large amounts of data change our lifestyle day-to-day. In this background, this paper proposes a fast blind deblurring algorithm for QR code images, which mainly achieves the effect of adaptive scale control by introducing an evaluation mechanism. Its main purpose is to solve the out-of-focus caused by lens shake, inaccurate focus, and optical noise by speeding up the latent image estimation in the process of multi-scale division iterative deblurring. The algorithm optimizes productivity under the guidance of collaborative computing, based on the characteristics of the QR codes, such as the features of gradient and strength. In the evaluation step, the Tenengrad method is used to evaluate the image quality, and the evaluation value is compared with the empirical value obtained from the experimental data. Combining with the error correction capability, the recognizable QR codes will be output. In addition, we introduced a scale control parameter to study the relationship between the recognition rate and restoration time. Theoretical analysis and experimental results show that the proposed algorithm has high recovery efficiency and well recovery effect, can be effectively applied in industrial applications