Physics-guided Noise Neural Proxy for Low-light Raw Image Denoising

Low-light raw image denoising plays a crucial role in mobile photography, and learning-based methods have become the mainstream approach. Training the learning-based methods with synthetic data emerges as an efficient and practical alternative to paired real data. However, the quality of synthetic data is inherently limited by the low accuracy of the noise model, which decreases the performance of low-light raw image denoising. In this paper, we develop a novel framework for accurate noise modeling that learns a physics-guided noise neural proxy (PNNP) from dark frames. PNNP integrates three efficient techniques: physics-guided noise decoupling (PND), physics-guided proxy model (PPM), and differentiable distribution-oriented loss (DDL). The PND decouples the dark frame into different components and handles different levels of noise in a flexible manner, which reduces the complexity of the noise neural proxy. The PPM incorporates physical priors to effectively constrain the generated noise, which promotes the accuracy of the noise neural proxy. The DDL provides explicit and reliable supervision for noise modeling, which promotes the precision of the noise neural proxy. Extensive experiments on public low-light raw image denoising datasets and real low-light imaging scenarios demonstrate the superior performance of our PNNP framework

Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation

In this paper, we present new data pre-processing and augmentation techniques for DNN-based raw image denoising. Compared with traditional RGB image denoising, performing this task on direct camera sensor readings presents new challenges such as how to effectively handle various Bayer patterns from different data sources, and subsequently how to perform valid data augmentation with raw images. To address the first problem, we propose a Bayer pattern unification (BayerUnify) method to unify different Bayer patterns. This allows us to fully utilize a heterogeneous dataset to train a single denoising model instead of training one model for each pattern. Furthermore, while it is essential to augment the dataset to improve model generalization and performance, we discovered that it is error-prone to modify raw images by adapting augmentation methods designed for RGB images. Towards this end, we present a Bayer preserving augmentation (BayerAug) method as an effective approach for raw image augmentation. Combining these data processing technqiues with a modified U-Net, our method achieves a PSNR of 52.11 and a SSIM of 0.9969 in NTIRE 2019 Real Image Denoising Challenge, demonstrating the state-of-the-art performance. Our code is available at https://github.com/Jiaming-Liu/BayerUnifyAug.Comment: Accepted by CVPRW 201

Over-the-Air Split Machine Learning in Wireless MIMO Networks

In split machine learning (ML), different partitions of a neural network (NN) are executed by different computing nodes, requiring a large amount of communication cost. To ease communication burden, over-the-air computation (OAC) can efficiently implement all or part of the computation at the same time of communication. Based on the proposed system, the system implementation over wireless network is introduced and we provide the problem formulation. In particular, we show that the inter-layer connection in a NN of any size can be mathematically decomposed into a set of linear precoding and combining transformations over MIMO channels. Therefore, the precoding matrix at the transmitter and the combining matrix at the receiver of each MIMO link, as well as the channel matrix itself, can jointly serve as a fully connected layer of the NN. The generalization of the proposed scheme to the conventional NNs is also introduced. Finally, we extend the proposed scheme to the widely used convolutional neural networks and demonstrate its effectiveness under both the static and quasi-static memory channel conditions with comprehensive simulations. In such a split ML system, the precoding and combining matrices are regarded as trainable parameters, while MIMO channel matrix is regarded as unknown (implicit) parameters.Comment: 15 pages, 13 figures, journal pape

Evaluation value of subjective visual quality examination on surgical indications of the early cataracts based on objective scatter index values

AimTo evaluate the subjective visual functions of early cataracts patients and assess their surgical indications.MethodsEyes were separated into a control group (Group A without cataract) and two early cataracts groups (Group B with 2.0 ≤ OSI < 3.0 and Group C with 3.0 ≤ OSI < 4.0). The objective scatter index (OSI), modulation transfer function cut-off frequency (MTF cut-off), and Strehl ratio (SR) values were applied to measure objective visual functions. The contrast sensitivity (CS) and scores of the questionnaires (QOL and VF-14) characterized subjective visual functions. Above visual functions were compared among three groups. Postoperative visual functions in Group B and C were analyzed to assess the outcome of surgery.ResultsNinety two subjects (126 eyes) were included in the study. All objective visual function in Group B were significantly better than Group C (all P < 0.01), but worse than Group A (all P < 0.01). Except for 1.5 c/d CS, subjective visual function in Group A were significantly better than Group B and C (all P < 0.05), but there was no significant differences between Group B and C. As for eyes that underwent surgery in Group B and C, all visual functions significantly improved after surgery (P < 0.05), except for 1.5 c/d CS in Group C. There were no significant differences among the three groups after surgery.ConclusionThe subjective visual function can be impaired in early cataracts patients with OSI < 3.0, whose objective visual functions were statistically better than patients with OSI ≥ 3.0. These patients can benefit equally from surgery as patients with OSI ≥ 3.0. Subjective visual functions can be used as surgical indications for these patients

A simulation method for finite non-stationary time series

When uploading content they are required to comply with their publisher agreement and the SHERPA RoMEO database to judge whether or not it is copyright safe to add this version of the paper to this repository. Abstract In this paper we propose a novel simulation method which enables us to obtain a large number of simulated time series cheaply. The developed method can be applied to any non-stationary time series of finite length and it guarantees that not only the marginal distributions but also the autocorrelation structures of observed and simulated time series are the same. Extensive simulation studies have been conducted to check the performance of our method and to assess if the overall dynamics of the observed time series is preserved by the simulated realizations. The developed simulation method has also been applied to the real size data of cocoon filament, which can be reeled from a cocoon produced by a silkworm. Very good results have been achieved in all the cases considered in the paper

A programmed cell death-related model based on machine learning for predicting prognosis and immunotherapy responses in patients with lung adenocarcinoma

Backgroundlung adenocarcinoma (LUAD) remains one of the most common and lethal malignancies with poor prognosis. Programmed cell death (PCD) is an evolutionarily conserved cell suicide process that regulates tumorigenesis, progression, and metastasis of cancer cells. However, a comprehensive analysis of the role of PCD in LUAD is still unavailable.MethodsWe analyzed multi-omic variations in PCD-related genes (PCDRGs) for LUAD. We used cross-validation of 10 machine learning algorithms (101 combinations) to synthetically develop and validate an optimal prognostic cell death score (CDS) model based on the PCDRGs expression profile. Patients were classified based on their median CDS values into the high and low-CDS groups. Next, we compared the differences in the genomics, biological functions, and tumor microenvironment of patients between both groups. In addition, we assessed the ability of CDS for predicting the response of patients from the immunotherapy cohort to immunotherapy. Finally, functional validation of key genes in CDS was performed.ResultsWe constructed CDS based on four PCDRGs, which could effectively and consistently stratify patients with LUAD (patients with high CDS had poor prognoses). The performance of our CDS was superior compared to 77 LUAD signatures that have been previously published. The results revealed significant genetic alterations like mutation count, TMB, and CNV were observed in patients with high CDS. Furthermore, we observed an association of CDS with immune cell infiltration, microsatellite instability, SNV neoantigens. The immune status of patients with low CDS was more active. In addition, CDS could be reliable to predict therapeutic response in multiple immunotherapy cohorts. In vitro experiments revealed that high DNA damage inducible transcript 4 (DDIT4) expression in LUAD cells mediated protumor effects.ConclusionCDS was constructed based on PCDRGs using machine learning. This model could accurately predict patients’ prognoses and their responses to therapy. These results provide new promising tools for clinical management and aid in designing personalized treatment strategies for patients with LUAD

Self-Assembly of a Virus-Mimicking Nanostructure System for Efficient Tumor-Targeted Gene Delivery

This is the published version, also available here: http://dx.doi.org/10.1089/10430340252792594.Molecular therapy, including gene therapy, is a promising strategy for the treatment of human disease. However, delivery of molecular therapeutics efficiently and specifically to the target tissue remains a significant challenge. A human transferrin (Tf)-targeted cationic liposome-DNA complex, Tf-lipoplex, has shown high gene transfer efficiency and efficacy with human head and neck cancer in vitro and in vivo (Xu, L., Pirollo, K.F., Tang, W.H., Rait, A., and Chang, E.H. Hum. Gene Ther. 1999;10:2941-2952). Here we explore the structure, size, formation process, and structure-function relationships of Tf-lipoplex. We have observed Tf-lipoplex to have a highly compact structure, with a relatively uniform size of 50-90 nm. This nanostructure is novel in that it resembles a virus particle with a dense core enveloped by a membrane coated with Tf molecules spiking the surface. More importantly, compared with unliganded lipoplex, Tf-lipoplex shows enhanced stability, improved in vivo gene transfer efficiency, and long-term efficacy for systemic p53 gene therapy of human prostate cancer when used in combination with conventional radiotherapy. On the basis of our observations, we propose a multistep self-assembly process and Tf-facilitated DNA cocondensation model that may provide an explanation for the resultant small size and effectiveness of our nanostructural Tf-lipoplex system