Towards Authentic Face Restoration with Iterative Diffusion Models and Beyond

An authentic face restoration system is becoming increasingly demanding in many computer vision applications, e.g., image enhancement, video communication, and taking portrait. Most of the advanced face restoration models can recover high-quality faces from low-quality ones but usually fail to faithfully generate realistic and high-frequency details that are favored by users. To achieve authentic restoration, we propose $\textbf{IDM}$, an $\textbf{I}$teratively learned face restoration system based on denoising $\textbf{D}$iffusion $\textbf{M}$odels (DDMs). We define the criterion of an authentic face restoration system, and argue that denoising diffusion models are naturally endowed with this property from two aspects: intrinsic iterative refinement and extrinsic iterative enhancement. Intrinsic learning can preserve the content well and gradually refine the high-quality details, while extrinsic enhancement helps clean the data and improve the restoration task one step further. We demonstrate superior performance on blind face restoration tasks. Beyond restoration, we find the authentically cleaned data by the proposed restoration system is also helpful to image generation tasks in terms of training stabilization and sample quality. Without modifying the models, we achieve better quality than state-of-the-art on FFHQ and ImageNet generation using either GANs or diffusion models.Comment: ICCV 202

DreamInpainter: Text-Guided Subject-Driven Image Inpainting with Diffusion Models

This study introduces Text-Guided Subject-Driven Image Inpainting, a novel task that combines text and exemplar images for image inpainting. While both text and exemplar images have been used independently in previous efforts, their combined utilization remains unexplored. Simultaneously accommodating both conditions poses a significant challenge due to the inherent balance required between editability and subject fidelity. To tackle this challenge, we propose a two-step approach DreamInpainter. First, we compute dense subject features to ensure accurate subject replication. Then, we employ a discriminative token selection module to eliminate redundant subject details, preserving the subject's identity while allowing changes according to other conditions such as mask shape and text prompts. Additionally, we introduce a decoupling regularization technique to enhance text control in the presence of exemplar images. Our extensive experiments demonstrate the superior performance of our method in terms of visual quality, identity preservation, and text control, showcasing its effectiveness in the context of text-guided subject-driven image inpainting

Taming Encoder for Zero Fine-tuning Image Customization with Text-to-Image Diffusion Models

This paper proposes a method for generating images of customized objects specified by users. The method is based on a general framework that bypasses the lengthy optimization required by previous approaches, which often employ a per-object optimization paradigm. Our framework adopts an encoder to capture high-level identifiable semantics of objects, producing an object-specific embedding with only a single feed-forward pass. The acquired object embedding is then passed to a text-to-image synthesis model for subsequent generation. To effectively blend a object-aware embedding space into a well developed text-to-image model under the same generation context, we investigate different network designs and training strategies, and propose a simple yet effective regularized joint training scheme with an object identity preservation loss. Additionally, we propose a caption generation scheme that become a critical piece in fostering object specific embedding faithfully reflected into the generation process, while keeping control and editing abilities. Once trained, the network is able to produce diverse content and styles, conditioned on both texts and objects. We demonstrate through experiments that our proposed method is able to synthesize images with compelling output quality, appearance diversity, and object fidelity, without the need of test-time optimization. Systematic studies are also conducted to analyze our models, providing insights for future work

Determination of 6 kinds of carbamate pesticides and 3 kinds of chloronicotinyl pesticides in Chinese Kushui rose by ultra high performance liquid chromatography-tandem mass spectrometry coupled with QuEChERS

Objective To establish a method for determination of 6 kinds of carbamate pesticides and 3 kinds of chloronicotinyl pesticides in Chinese Kushui rose by ultra high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) coupled with QuEChERS. Methods After extracted by acetonitrile, the Chinese Kushui rose was cleaned by QuEChERS. The target compounds were separated by C18 column (2.1 mm×100 mm, 1.7 μm) using 10 mmol/L ammonium acetate solution (0.1% formic acid) with acetonrtrile as mobile phase for gradient elution, and analyzed by MS/MS system with electrospray ionization (ESI+) under muti-reaction monitoring mode and quantified by external standard method. Results All the 9 kinds of pesticides showed good linear relationships in range of 0.01-0.50 μg/mL, and the correlation coefficients were above 0.990, the recoveries at different spiked levels for all target compounds in blank matrices were 76.3%-102%, and the relative standard deviation (RSD) were 1.3%-9.0% (n=6). The limits of detection and quantification of the method were 0.001 6-0.003 2 and 0.005 4-0.010 mg/kg. Conclusion The method was suitable for rapid screening and analysis of 9 pesticide residues in Chinese Kushui rose with the advantage of accuracy, rapidity, simplicity and high sensitivity

Service Performance Evaluation of Operating Loess Railway Tunnel Based on Bayesian Network

Due to the particularity of loess engineering geology, loess railway tunnel accidents occur frequently. Based on the theoretical basis of risk management, this study evaluated the service performance of loess railway tunnels. Based on the improved TOPSIS method, the indirect proximity degree of each risk factor was compared, and the appropriate service performance evaluation index was selected. Based on the ISM model and the causality graph modification method, the dependency relationship between nodes was obtained and the Bayesian network evaluation model was constructed. By constructing the database, the EM algorithm was used for data learning, and the model was trained and verified, and the overall accuracy (ACC) and F1 value are used to comprehensively evaluate the training and prediction effect of the model. Finally, the evaluation model was applied to a tunnel case. The results show that the established Bayesian network model has a high accuracy of 92%, which is easy to operate, effective and practical, and it is also applicable in situations of incomplete index statistical data

Air horizontal jets into quiescent water

Gas submerged jet is an outstanding thermohydraulic phenomenon in pool scrubbing of fission products during a severe nuclear accident. Experiments were performed on the hydraulic characteristics in the ranges of air mass flux 0.1–1400 kg/m2s and nozzle diameter 10–80 mm. The results showed that the dependence of inlet pressure on the mass flux follows a power law in subsonic jets and a linear law in sonic jets. The effect of nozzle submerged depth was negligible. The isolated bubbling regime, continuous bubbling regime, transition regime, and jetting regime were observed in turn, as the mass flux increased. In the bubbling regime and jetting regime, the air volume fraction distribution was approximately symmetric in space. Themelis model could capture the jet trajectory well. In the transition regime, the air volume fraction distribution loses symmetry due to the bifurcated secondary plume. The Li correlation and Themelis model showed sufficient accuracy for the prediction of jet penetration length

Microscopic Mechanism on the Heat Conduction of Organic Liquids: A Molecular Dynamics Study

The research on energy conversion and transportation of fuels at a microscopic level is of great significance to the development of industry. As a new alternative fuel, alcohols are widely used in industry and daily life, so it is necessary to investigate the thermophysical properties of them. In this work, seven species of pure liquid alcohols were performed to investigate the microscopic mechanisms of thermal energy transfer by non-equilibrium molecular dynamic (NEMD) method. Firstly, the thermal conductivity of alcohols was calculated and was found to be consistent with the experimental data. Then, the influence of temperature on energy transfer is investigated, the results show that the contribution of convection energy transfer increases and both the inter- and intramolecular terms decrease with the increase of temperature. Finally, the influence of molecular length on energy transfer was investigated at the same temperature, and it is concluded that the contribution of the convective term decreases and the interactive term increases to the total heat flux with increasing the length of the chain. It is worth mentioning that the contribution of intramolecular energy transfer gradually becomes a dominant part of the total energy transfer as the linear chain molecule increases to a certain length and the number of carbon atoms at the intersection point of inter- and intramolecular energy transfer is similar to the turning point of thermal conductivity

Lithium-Ion Battery Prognostics with Hybrid Gaussian Process Function Regression

The accurate prognostics of lithium-ion battery state of health (SOH) and remaining useful life (RUL) have great significance for reducing the costs of maintenance. The methods based on the physical models cannot perform satisfactorily as the systems become more and more complex. With the development of digital acquisition and storage technology, the data of battery cells can be obtained. This makes the data-driven methods get more and more attention. In this paper, to overcome the problem that the trend fitting deteriorates rapidly when test data are far from the training data for multiple-step-ahead estimation, a prognostic method fusing the wavelet de-noising (WD) method and the hybrid Gaussian process function regression (HGPFR) model for predicting the RUL of the lithium-ion battery is proposed. Gaussian process regression (GPR) is a typical representative for the Bayesian structure with non-parameter expression and uncertainty presentation. In this case, the effects on predictive results are compared and analyzed using the proposed method and the HGPFR model with different lengths of training data. Besides, in consideration of the degradation characteristics for the lithium-ion battery data set, the selections of the wavelet de-noising method are performed with corresponding experimental analyses. Furthermore, we set the hype-parameter for the mean function and co-variance function, and then develop a method for parameter optimization to make the proposed model suitable for the data. Moreover, a numerical simulation based on the data repository of Department of Engineering Science (DES) university of Oxford and Center for Advanced Life Cycle Engineering (CALCE) of University of Maryland is carried out, and the results are analyzed. For the data repository, an accuracy of 2.2% is obtained compared with the same value of 6.7% for the HGPFR model. What is more, the applicability and stability are verified with the prognostic results by the proposed method

Effects of Edge Directions on the Structural Controllability of Complex Networks

<div><p>Recent advances indicate that assigning or reversing edge direction can significantly improve the structural controllability of complex networks. For directed networks, approaching the optimal structural controllability can be achieved by detecting and reversing certain “inappropriate” edge directions. However, the existence of multiple sets of “inappropriate” edge directions suggests that different edges have different effects on optimal controllability—that is, different combinations of edges can be reversed to achieve the same structural controllability. Therefore, we classify edges into three categories based on their direction: <i>critical</i>, <i>redundant</i> and <i>intermittent</i>. We then investigate the effects of changing these edge directions on network controllability, and demonstrate that the existence of more critical edge directions implies not only a lower cost of modifying inappropriate edges but also better controllability. Motivated by this finding, we present a simple edge orientation method aimed at producing more critical edge directions—utilizing only local information—which achieves near optimal controllability. Furthermore, we explore the effects of edge direction on the controllability of several real networks.</p></div