Learning Image-Adaptive Codebooks for Class-Agnostic Image Restoration

Recent work on discrete generative priors, in the form of codebooks, has shown exciting performance for image reconstruction and restoration, as the discrete prior space spanned by the codebooks increases the robustness against diverse image degradations. Nevertheless, these methods require separate training of codebooks for different image categories, which limits their use to specific image categories only (e.g. face, architecture, etc.), and fail to handle arbitrary natural images. In this paper, we propose AdaCode for learning image-adaptive codebooks for class-agnostic image restoration. Instead of learning a single codebook for each image category, we learn a set of basis codebooks. For a given input image, AdaCode learns a weight map with which we compute a weighted combination of these basis codebooks for adaptive image restoration. Intuitively, AdaCode is a more flexible and expressive discrete generative prior than previous work. Experimental results demonstrate that AdaCode achieves state-of-the-art performance on image reconstruction and restoration tasks, including image super-resolution and inpainting

The Application of Downhole Vibration Factor in Drilling Tool Reliability Big Data Analytics - A Review

In the challenging downhole environment, drilling tools are normally subject to high temperature, severe vibration, and other harsh operation conditions. The drilling activities generate massive field data, namely field reliability big data (FRBD), which includes downhole operation, environment, failure, degradation, and dynamic data. Field reliability big data has large size, high variety, and extreme complexity. FRBD presents abundant opportunities and great challenges for drilling tool reliability analytics. Consequently, as one of the key factors to affect drilling tool reliability, the downhole vibration factor plays an essential role in the reliability analytics based on FRBD. This paper reviews the important parameters of downhole drilling operations, examines the mode, physical and reliability impact of downhole vibration, and presents the features of reliability big data analytics. Specifically, this paper explores the application of vibration factor in reliability big data analytics covering tool lifetime/failure prediction, prognostics/diagnostics, condition monitoring (CM), and maintenance planning and optimization. Furthermore, the authors highlight the future research about how to better apply the downhole vibration factor in reliability big data analytics to further improve tool reliability and optimize maintenance planning

Generalization and Hallucination of Large Vision-Language Models through a Camouflaged Lens

Large Vision-Language Model (LVLM) has seen burgeoning development and increasing attention recently. In this paper, we propose a novel framework, camo-perceptive vision-language framework (CPVLF), to explore whether LVLM can generalize to the challenging camouflaged object detection (COD) scenario in a training-free manner. During the process of generalization, we find that due to hallucination issues within LVLM, it can erroneously perceive objects in camouflaged scenes, producing counterfactual concepts. Moreover, as LVLM is not specifically trained for the precise localization of camouflaged objects, it exhibits a degree of uncertainty in accurately pinpointing these objects. Therefore, we propose chain of visual perception, which enhances LVLM's perception of camouflaged scenes from both linguistic and visual perspectives, reducing the hallucination issue and improving its capability in accurately locating camouflaged objects. We validate the effectiveness of CPVLF on three widely used COD datasets, and the experiments show the potential of LVLM in the COD task

Real-time Controllable Denoising for Image and Video

Controllable image denoising aims to generate clean samples with human perceptual priors and balance sharpness and smoothness. In traditional filter-based denoising methods, this can be easily achieved by adjusting the filtering strength. However, for NN (Neural Network)-based models, adjusting the final denoising strength requires performing network inference each time, making it almost impossible for real-time user interaction. In this paper, we introduce Real-time Controllable Denoising (RCD), the first deep image and video denoising pipeline that provides a fully controllable user interface to edit arbitrary denoising levels in real-time with only one-time network inference. Unlike existing controllable denoising methods that require multiple denoisers and training stages, RCD replaces the last output layer (which usually outputs a single noise map) of an existing CNN-based model with a lightweight module that outputs multiple noise maps. We propose a novel Noise Decorrelation process to enforce the orthogonality of the noise feature maps, allowing arbitrary noise level control through noise map interpolation. This process is network-free and does not require network inference. Our experiments show that RCD can enable real-time editable image and video denoising for various existing heavy-weight models without sacrificing their original performance.Comment: CVPR 202

Applications and technological challenges for heat recovery, storage and utilisation with latent thermal energy storage

Thermal energy storage (TES) technology is considered to have the greatest potential to balance the demand and supply overcoming the intermittency and fluctuation nature of real-world heat sources, making a more flexible, highly efficient and reliable thermal energy system. This article provides a comprehensive state-of-the-art review of latent thermal energy storage (LTES) technology with a particular focus on medium-high temperature phase change materials for heat recovery, storage and utilisation. This review aims to identify potential methods to design and optimise LTES heat exchangers for heat recovery and storage, bridging the knowledge gap between the present studies and future technological developments. The key focuses of current work can be described as follows: (1) Insight into moderate-high temperature phase change materials and thermal conductivity enhancement methods. (2) Various configurations of latent thermal energy storage heat exchangers and relevant heat transfer enhancement techniques (3) Applications of latent thermal energy storage heat exchangers with different thermal sources, including solar energy, industrial waste heat and engine waste heat, are discussed in detail

Revisiting Single Image Reflection Removal In the Wild

This research focuses on the issue of single-image reflection removal (SIRR) in real-world conditions, examining it from two angles: the collection pipeline of real reflection pairs and the perception of real reflection locations. We devise an advanced reflection collection pipeline that is highly adaptable to a wide range of real-world reflection scenarios and incurs reduced costs in collecting large-scale aligned reflection pairs. In the process, we develop a large-scale, high-quality reflection dataset named Reflection Removal in the Wild (RRW). RRW contains over 14,950 high-resolution real-world reflection pairs, a dataset forty-five times larger than its predecessors. Regarding perception of reflection locations, we identify that numerous virtual reflection objects visible in reflection images are not present in the corresponding ground-truth images. This observation, drawn from the aligned pairs, leads us to conceive the Maximum Reflection Filter (MaxRF). The MaxRF could accurately and explicitly characterize reflection locations from pairs of images. Building upon this, we design a reflection location-aware cascaded framework, specifically tailored for SIRR. Powered by these innovative techniques, our solution achieves superior performance than current leading methods across multiple real-world benchmarks. Codes and datasets will be publicly available

Performance of the fixed-point autoencoder

Model autodavača (autoencodera) je jedan od najtipičnijih modela temeljitog učenja koji se najčešće koriste u učenju neupravljačkog obilježja za mnoge aplikacije kao što su prepoznavanje, identifikacija i pretraživanje. Algoritmi autodavača predstavljaju opsežne računarske zadatke. Stvaranje opsežnog modela autodavača može zadovoljiti potrebe u analizi ogromnog broja podataka. Međutim, vrijeme učenja katkada postaje nepodnošljivo, što dovodi do potrebe istraživanja nekih platformi hardvera za ubrzavanje, kao što je FPGA. Verzije softvera autodavača često koriste izraze jednostruke ili dvostruke preciznosti. Ali implementiranje jedinica s promjenjivom točkom je vrlo skupo za postavljanje u FPGA. Kod implementacije autodavača na hardver stoga se često primjenjuje aritmetika nepromjenjive točke. No često se zanemaruje gubitak točnosti i nije proučavan u ranijim radovima. Ima tek nekoliko radova koji se bave akceleratorima koji koriste fiksne širine bita na drugim modelima neuronskih mreža. U našem se radu daje opsežna procjena prikaza preciznosti implikacija nepromjenjive točke na autodavač, postizanje najbolje značajke i područja učinkovitosti. Metoda konverzije formata podataka, metode blokiranja matrice i aproksimacija kompleksnim funkcijama predstavljaju ključne razmatrane čimbenike u skladu s mjestom implementacije hardvera. U radu se procjenjuju metoda simulacije konverzije podataka, blokiranje matrice različitim paralelizmom i jednostavna metoda evaluacije. Rezultati su pokazali da je širina bita s nepromjenjivom točkom uistinu utjecala na učinkovitost autodavača. Višestruki čimbenici mogu postići suprotan učinak. Svaki čimbenik može imati dvostruki učinak odbacivanja "brojnih" informacija i "korisnih" informacija u isto vrijeme. Područje predstavljanja treba pažljivo odabrati u skladu s računarskim paralelizmom. Rezultat je također pokazao da se primjenom aritmetike nepromjenjive točke može garantirati preciznost algoritma autodavača i postići prihvatljiva brzina konvergencije.The model of autoencoder is one of the most typical deep learning models that have been mainly used in unsupervised feature learning for many applications like recognition, identification and mining. Autoencoder algorithms are compute-intensive tasks. Building large scale autoencoder model can satisfy the analysis requirement of huge volume data. But the training time sometimes becomes unbearable, which naturally leads to investigate some hardware acceleration platforms like FPGA. The software versions of autoencoder often use single-precision or double-precision expressions. But the floating point units are very expensive to implement on FPGA. Fixed-point arithmetic is often used when implementing autoencoder on hardware. But the accuracy loss is often ignored and its implications for accuracy have not been studied in previous works. There are only some works focused on accelerators using some fixed bit-widths on other neural networks models. Our work gives a comprehensive evaluation to demonstrate the fix-point precision implications on the autoencoder, achieving best performance and area efficiency. The method of data format conversion, the matrix blocking methods and the complex functions approximation are the main factors considered according to the situation of hardware implementation. The simulation method of the data conversion, the matrix blocking with different parallelism and a simple PLA approximation method were evaluated in this paper. The results showed that the fixed-point bit-width did have effect on the performance of autoencoder. Multiple factors may have crossed effect. Each factor would have two-sided impacts for discarding the "abundant" information and the "useful" information at the same time. The representation domain must be carefully selected according to the computation parallelism. The result also showed that using fixed-point arithmetic can guarantee the precision of the autoencoder algorithm and get acceptable convergence speed

MIPI 2022 Challenge on RGBW Sensor Re-mosaic: Dataset and Report

Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. In this paper, RGBW Joint Remosaic and Denoise, one of the five tracks, working on the interpolation of RGBW CFA to Bayer at full resolution, is introduced. The participants were provided with a new dataset including 70 (training) and 15 (validation) scenes of high-quality RGBW and Bayer pairs. In addition, for each scene, RGBW of different noise levels was provided at 0dB, 24dB, and 42dB. All the data were captured using an RGBW sensor in both outdoor and indoor conditions. The final results are evaluated using objective metrics including PSNR, SSIM, LPIPS, and KLD. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://github.com/mipi-challenge/MIPI2022.Comment: ECCV 2022 Mobile Intelligent Photography and Imaging (MIPI) Workshop--RGBW Sensor Re-mosaic Challenge Report. MIPI workshop website: http://mipi-challenge.org/. arXiv admin note: substantial text overlap with arXiv:2209.07060, arXiv:2209.07530, arXiv:2209.0705

Are the preoperative albumin levels and the albumin to fibrinogen ratio the risk factors for acute infection after primary total joint arthroplasty?

BackgroundAcute infection, such as periprosthetic joint infection and superficial surgical site infection, after primary total joint arthroplasty (TJA) is a serious complication, and its risk factors remain controversial. This study aimed to identify the risk factors for acute infection after primary TJA, especially the serological indicators that reflect preoperative nutritional statuses, such as albumin level and albumin to fibrinogen ratio (AFR).MethodsWe retrospectively reviewed patients who underwent elective primary hip or knee arthroplasty at our institution from 2009 to 2021. Potential risk factors of acute infection and demographic information were extracted from an electronic health record. Patients who suffered acute infection, such as PJI or SSI, after TJA were considered the study group. Non-infected patients were matched 1:2 with the study group according to sex, age, the involved joint (hip or knee), and year of surgery (control group). The variables of potential risk factors for acute postoperative infection (demographic characteristics, preoperative comorbidities and drug use, operative variables, and laboratory values) were collected and evaluated by regression analysis. Restrictive cubic spline regression analysis was also used to examine the relationship between preoperative serum albumin levels and acute postoperative infection.ResultsWe matched 162 non-infected patients with 81 patients who suffered from acute postoperative infection. Among the patients who suffered from acute infection within 90 days after TJA, 18 were diagnosed with periprosthetic joint infection and 63 with surgical site infection. Low albumin levels were strongly associated with acute postoperative infection (95% confidence interval, 0.822–0.980; P = 0.015). This risk increased as preoperative albumin levels decreased, with a negative dose-response relationship (Poverall = 0.002; Pnonlinear = 0.089). However, there was no significant association between the AFR and acute infection after primary TJA (P = 0.100).ConclusionThere is currently insufficient evidence to confirm the relationship between preoperative AFR and acute infection after elective primary TJA, while a lower preoperative albumin level is an independent risk factor for acute infection with a negative dose-response relationship. This suggests that optimal nutritional management may be benefited before elective primary TJA