Internet of Things (IoT) devices generate large volumes of time series data that are often volatile and complex, making probabilistic time series forecasting (TSF) essential for modeling the distribution of future outcomes. Recently, diffusion-based TSF methods have gained attention for their ability to learn complex distributions. However, they typically apply the diffusion process directly in the time domain, which may struggle to capture complex temporal dependencies, thus limiting the full potential of the diffusion process. Besides, they obtain probabilistic forecasts by sampling multiple plausible outcomes from the learned distribution, which is time-consuming and less effective. To solve these problems, we propose Invertible Latent Diffusion for probabilistic Time Series Forecasting (IL-DiffTSF), a novel approach based on a latent diffusion model. Specifically, we design an invertible latent projection between time series and latent space, where a conditional diffusion process is applied. This design ensures bidirectional consistency and minimal information loss, enabling more accurate TSF. Moreover, instead of sampling-based probabilistic forecasting, IL-DiffTSF represents uncertainties by directly learning a mapping from latent representations to prediction errors, achieving faster and more reliable uncertainty estimates. Experiments on univariate and multivariate benchmarks validate the efficiency and effectiveness of IL-DiffTSF. The code for this project is available at https://github.com/vanerkz/IL-DiffTSF.

Reconstruction of hyperspectral images (HSIs) from their RGB measurements is an ill-posed inverse problem. The key to successful reconstruction relies on establishing an effective HSI prior, for which deep learning techniques have achieved impressive performance. However, the scarcity of large-scale HSI datasets poses a significant challenge, limiting the practical application of deep HSI reconstruction methods and often leading to incorrect results when dealing with unseen substances or materials. To tackle this challenge, we propose a deep RGB-to-HSI reconstruction model based on the sparse prior of hyperspectral signals. The network can effectively correlate HSI and RGB features via shared sparse codes, representing the weights of spectral-unique materials. Besides, testing images with unseen materials can be detected by measuring their sparse modeling errors. Experimental results demonstrate that the proposed method achieves promising results on RGB-to-HSI reconstruction. Further, the sparse modeling error evidently demonstrates its efficacy as an indicator for unseen materials.

Radar-based human detection draws significant attention in response to growing safety concerns driven by advances in factory automation and smart home technologies. However, much of this research typically operates in controlled environments characterized by minimal clutter and noise, which limits their effectiveness in real-world scenarios such as urban areas, factories, and smart homes. In this study, we address this limitation by collecting a real-world radar dataset in dynamic and cluttered indoor environments, spanning five distinctive environments. To simulate non-human targets, we introduce a moving trolley. Subsequently, we propose a system including simple Radar Signal Processing steps and a learning-based model using unsupervised domain adaptation to enhance its adaptability to unseen environments. Through a series of comprehensive experiments employing popular learning-based methods on our dataset, we demonstrate the model’s efficacy in mitigating environmental interference and successfully adapting to previously unseen environments.

Arbitrary style transfer aims to render artistic features from a style reference onto an image while retaining its original content. Previous methods either focus on learning the holistic style from a specific artist or extracting instance features from a single artwork. However, they often fail to apply style elements uniformly across the entire image and lack adaptation to the style of different artworks. To solve these issues, our key insight is that the art genre has better generality and adaptability than the overall features of the artist. To this end, we propose a Dual-head Genre-instance Transformer (DGiT) framework to simultaneously capture the genre and instance features for arbitrary style transfer. To the best of our knowledge, this is the first work to integrate the genre features and instance features to generate a high-quality stylized image. Moreover, we design two contrastive losses to enhance the capability of the network to capture two style features. Our approach ensures the uniform distribution of the overall style across the stylized image while enhancing the details of textures and strokes in local regions. Qualitative and quantitative evaluations demonstrate that our approach exhibits superior visual quality and efficiency.

Blind deblurring has received considerable attention in recent years. However, state-of-the-art methods often fail to process saturated blurry images. The main reason is that pixels around saturated regions are not conforming to the commonly used linear blur model. Pioneer arts suggest excluding these pixels during the deblurring process, which sometimes simultaneously removes the informative edges around saturated regions and results in insufficient information for kernel estimation when large saturated regions exist. To address this problem, we introduce a new blur model to fit both saturated and unsaturated pixels, and all informative pixels can be considered during the deblurring process. Based on our model, we develop an effective maximum a posterior (MAP)-based optimization framework. Quantitative and qualitative evaluations on benchmark datasets and challenging real-world examples show that the proposed method performs favorably against existing methods.

Deblurring night blurry images is difficult, because the common-used blur model based on the linear convolution operation does not hold in this situation due to the influence of saturated pixels. In this paper, we propose a non-blind deblurring network (NBDN) to restore night blurry images. To mitigate the side effects brought by the pixels that violate the blur model, we develop a confidence estimation unit (CEU) to estimate a map which ensures smaller contributions of these pixels in the deconvolution steps which are optimized by the conjugate gradient (CG) method. Moreover, unlike the existing methods using manually tuned hyper-parameters in their frameworks, we propose a hyper-parameter estimation unit (HPEU) to adaptively estimate hyper-parameters for better image restoration. The experimental results demonstrate that the proposed network performs favorably against state-of-the-art algorithms both quantitatively and qualitatively.

The success of existing face deblurring methods based on deep neural networks is mainly due to the large model capacity. Few algorithms have been specially designed according to the domain knowledge of face images and the physical properties of the deblurring process. In this paper, we propose an effective face deblurring algorithm based on deep convolutional neural networks (CNNs). Motivated by the conventional deblurring process which usually involves the motion blur estimation and the latent clear image restoration, the proposed algorithm first estimates motion blur by a deep CNN and then restores latent clear images with the estimated motion blur. However, estimating motion blur from blurry face images is difficult as the textures of the blurry face images are scarce. As most face images share some common global structures which can be modeled well by sketch information, we propose to learn face sketches by a deep CNN so that the sketches can help the motion blur estimation. With the estimated motion blur, we then develop an effective latent image restoration algorithm based on a deep CNN. Although involving the several components, the proposed algorithm is trained in an end-to-end fashion. We analyze the effectiveness of each component on face image deblurring and show that the proposed algorithm is able to deblur face images with favorable performance against state-of-the-art methods.