To achieve structured inference, the model capitalizes on the powerful mapping between input and output in CNN networks, while simultaneously benefiting from the long-range interactions in CRF models. By training CNN networks, rich priors for both unary and smoothness terms are acquired. MFIF's structured inference is attained using the expansion graph-cut algorithm. The networks for both CRF terms are trained using a dataset that includes both clean and noisy image pairs. A low-light MFIF dataset is also created to exemplify the genuine noise introduced by the camera's sensor in real-world scenarios. The combined qualitative and quantitative results demonstrate that mf-CNNCRF's performance exceeds that of state-of-the-art MFIF methods, both for clean and noisy input images, displaying a greater resilience to different noise types without any reliance on prior noise characteristics.
X-ray imaging, a prevalent technique in art investigation, utilizes X-radiography. Insights into the artist's creative process and the condition of the painting can be discovered, often unveiling information about hidden aspects of their work and methods. When X-raying paintings on both sides, a superimposed X-ray image is obtained, and this paper explores methods for separating this composite image. We propose a novel neural network architecture, constructed from interconnected autoencoders, to disintegrate a composite X-ray image into two simulated images, each corresponding to a side of the painting, using the RGB color images from either side. Nucleic Acid Electrophoresis Gels The encoders of this auto-encoder structure, developed with convolutional learned iterative shrinkage thresholding algorithms (CLISTA) employing algorithm unrolling, are linked to simple linear convolutional layers that form the decoders. The encoders interpret sparse codes from the visible images of the front and rear paintings and a superimposed X-ray image. The decoders subsequently reproduce the original RGB images and the combined X-ray image. The learning algorithm is entirely self-supervised, requiring no dataset containing both composite and individual X-ray images. Hubert and Jan van Eyck's 1432 painting of the Ghent Altarpiece's double-sided wing panels provided the visual data for testing the methodology. These trials definitively prove that the proposed method excels in X-ray image separation for art investigation, surpassing all other current state-of-the-art techniques.
The light-scattering and absorption properties of underwater impurities negatively impact underwater image quality. Data-driven underwater image enhancement methods are presently restricted by the limited availability of extensive datasets, inclusive of diverse underwater scenes and high-resolution reference images. In addition, the variable attenuation observed in different color channels and spatial areas is not fully integrated into the enhanced result. A significant contribution of this work is a large-scale underwater image (LSUI) dataset, which outperforms existing underwater datasets by featuring a wider range of underwater scenes and better visual reference images. Real-world underwater image groups, totaling 4279, are contained within the dataset. Each raw image is paired with its clear reference image, semantic segmentation map, and medium transmission map. Our study also presented the U-shaped Transformer network, with a transformer model being implemented for the UIE task, marking its initial use. A channel-wise multi-scale feature fusion transformer (CMSFFT) module and a spatial-wise global feature modeling transformer (SGFMT) module, tailored for the UIE task, are incorporated into the U-shaped Transformer architecture. These modules strengthen the network's attention to color channels and spatial areas, applying more significant attenuation. In pursuit of enhanced contrast and saturation, a unique loss function combining RGB, LAB, and LCH color spaces, inspired by human vision, is created. In a series of extensive experiments on available datasets, the reported technique has been proven to outperform the existing state-of-the-art, exhibiting an improvement of over 2dB. The Bian Lab's website at https//bianlab.github.io/ features the downloadable dataset and demo code.
Despite the impressive progress in active learning methodologies for image recognition, a thorough investigation into instance-level active learning for object detection is conspicuously absent. To facilitate informative image selection in instance-level active learning, this paper proposes a multiple instance differentiation learning (MIDL) approach that integrates instance uncertainty calculation with image uncertainty estimation. MIDL's functionalities are based on two modules: a classifier prediction differentiation module and a module dedicated to the differentiation of multiple instances. Through the application of two adversarial instance classifiers, trained on labeled and unlabeled data, the system calculates the uncertainty of the unlabeled data instances. Using a multiple instance learning paradigm, the latter methodology treats unlabeled images as bags of instances and refines the estimation of image-instance uncertainty leveraging the predictions of the instance classification model. MIDL's Bayesian approach integrates image uncertainty with instance uncertainty, calculated by weighting instance uncertainty using instance class probability and instance objectness probability, all under the total probability formula. Thorough experimentation affirms that MIDL establishes a strong foundation for active learning at the level of individual instances. On standard object detection datasets, this method demonstrably surpasses other cutting-edge techniques, especially when the training data is limited. Translation The code's location on the internet is: https://github.com/WanFang13/MIDL.
The substantial accumulation of data creates the need to conduct comprehensive data clustering procedures. The bipartite graph theory is widely used to craft scalable algorithms that depict the interrelationships between samples and a limited number of anchors, thereby eschewing a pairwise linking approach. However, existing spectral embedding methods, along with bipartite graph approaches, do not incorporate the explicit learning of cluster structures. Post-processing, including the application of K-Means, is crucial for obtaining cluster labels. Notwithstanding, prevailing anchor-based methodologies usually acquire anchors via K-Means clustering or the random selection of a small number of samples; these methods, while time-saving, commonly suffer from volatile performance. This paper focuses on the critical components of scalability, stability, and integration within the context of large-scale graph clustering. A graph learning model, structured around clusters, is proposed to produce a c-connected bipartite graph and provide direct access to discrete labels, with c denoting the cluster number. Considering data features or pairwise relations as a basis, we proceeded to devise an anchor selection strategy that is independent of initialization. The proposed method's efficacy, as evidenced by trials using synthetic and real-world datasets, surpasses that of competing techniques.
Non-autoregressive (NAR) generation, initially employed in neural machine translation (NMT) to optimize inference speed, has become a subject of substantial attention in both machine learning and natural language processing. 5-Ethynyl-2′-deoxyuridine Machine translation inference speed can be considerably augmented by NAR generation, but this enhancement comes with a trade-off in translation accuracy relative to autoregressive generation. The past few years have seen the creation of many new models and algorithms, intended to overcome the accuracy disparity between NAR and AR generation. We offer a systematic survey in this paper, comparing and contrasting different types of non-autoregressive translation (NAT) models, highlighting diverse aspects. NAT's activities are grouped into several classifications, including data transformation, modeling techniques, training criteria, decoding algorithms, and the advantages from pre-trained models. Furthermore, the applicability of NAR models will be explored beyond machine translation, including their utilization in grammatical error correction, text summarization, text style adaptation, dialogue systems, semantic interpretation, automatic speech recognition, and various other areas. Subsequently, we explore potential future research avenues, encompassing the removal of KD dependencies, the formulation of appropriate training goals, the pre-training of NAR models, and a range of wider applications, and so on. We expect this survey to assist researchers in recording the latest advancements in NAR generation, motivate the design of cutting-edge NAR models and algorithms, and allow industry practitioners to select appropriate solutions for their specific needs. The web page for this survey is linked here: https//github.com/LitterBrother-Xiao/Overview-of-Non-autoregressive-Applications.
A new multispectral imaging technique is presented here. This technique fuses fast high-resolution 3D magnetic resonance spectroscopic imaging (MRSI) and fast quantitative T2 mapping. The approach seeks to capture and evaluate the complex biochemical alterations within stroke lesions and assess its potential for predicting stroke onset time.
Fast trajectories and sparse sampling were combined in specialized imaging sequences to acquire whole-brain maps of both neurometabolites (203030 mm3) and quantitative T2 values (191930 mm3) within a 9-minute scan period. The study involved participants who presented with ischemic stroke at the hyperacute (0-24 hours, n=23) or acute (24-7 days, n=33) timeframes. The study examined differences in lesion N-acetylaspartate (NAA), lactate, choline, creatine, and T2 signals between groups, while also investigating the correlation with patients' symptomatic duration. Bayesian regression analyses compared the predictive models of symptomatic duration derived from multispectral signals.