Light field (LF) imaging has attracted increasing research interest in challenging illumination conditions due to its ability to provide rich spatial and angular cues. However, such data present dual challenges: 1) the inherent multi-view structure introduces substantial data redundancy, creating high demands for efficient compression; 2) the insufficient illumination leads to severe quality degradation, which weakens inter-view consistency and visual perception. To address these coupled factors, we propose a Probabilistic-based learning for joint LF image compression and enhancement under low-light conditions (PrL-LFCE). The framework unifies structure-aware compression and feature enhancement mechanisms by introducing learnable probabilistic modeling into both feature coupling and latent distribution estimation to adaptively handle the uncertainty induced by illumination degradation and compression-related information loss. Specifically, we design a probability-based multi-directional feature coupling module that dynamically balances structural preservation and redundancy reduction across multiple directionally arranged sub-aperture images. Moreover, we introduce a swin-gated enhancement module that suppresses noise and highlights structurally salient regions in compression-aware feature representations through attention-guided gating. Extensive experiments show that PrL-LFCE consistently outperforms state-of-the-art methods, achieving at least 34.86% bitrate savings while maintaining excellent visual quality, demonstrating a strong joint compression and enhancement capability.

Eliminating the effect of input saturation is greatly significant in maintaining system performance. This article proposes a performance-based adaptive anti-saturation control scheme for a class of uncertain nonlinear systems with input saturation limit. Unlike the compensation-based anti-saturation schemes that do not take saturation occurrence into account, the proposed method adopts a monitoring modulation approach to proactively prevent the occurrence of input saturation. Specifically, an adaptive controller is first designed based on prescribed performances and shifting functions. Then, a monitoring module is employed to supervise the behavior of the control signal, and a modulation module is intended to implement the controller reconfiguration by updating the shifting functions. It is shown that by using the proposed scheme, the control signal never violates the saturation limit, the Zeno phenomenon is avoided, and the tracking error satisfies a modified prescribed performance.

With the wide application of imaging technologies in low-light scenarios such as night surveillance and astronomical observation, low-light light field (LF) image has become increasingly critical. However, such imaging data face dual challenges. First, the multi-view structure inherently requires high storage and transmission bandwidth, as well as efficient compression. Second, under limited illumination conditions, the reconstructed images exhibit significant noise, low contrast, and detail loss, which severely compromise enhancement performance. Current approaches typically process compression and enhancement sequentially, which suffers from two critical drawbacks: (a) compression artifacts tend to eliminate high-frequency components essential for subsequent enhancement, and (b) existing enhancement algorithms demonstrate limited robustness when handling heavily compressed images. To bridge this gap, we propose a joint compression-enhancement framework that integrates epipolar plane image (EPI) structure-aware modeling into the compression process and incorporates feature-guided restoration during the decoding stage, thereby optimizing the bitrate-distortion-perception trade-off. Specifically, the EPI dual-axis attention (EDA) module is proposed to exploit the horizontal and vertical EPI consistency structures extracted along sub-aperture image (SAI) sequences. Furthermore, the Decoder-side Implicit Detail Restoration (D-IDR) module is designed to restore detailed features. This restoration is enabled by continuous implicit mapping within the compressed latent space, which integrates coordinate-based positional encoding and frequency-domain feature modeling. Experiments on public low-light LF datasets demonstrate that our method outperforms state-of-the-art (SOTA) methods, achieving up to 37.40 % bit-rate savings while maintaining superior structural preservation and perceptual quality.

Maps in the form of digital images are widely available in geoportals, Web pages, and other data sources. The metadata of map images, such as spatial extents and place names, are critical for their indexing and searching. However, many map images have either mismatched metadata or no metadata at all. Recent developments in deep learning offer new possibilities for enriching the metadata of map images via image-based information extraction. One major challenge of using deep learning models is that they often require large amounts of training data that have to be manually labeled. To address this challenge, this paper presents a deep learning approach with GIS-based data augmentation that can automatically generate labeled training map images from shapefiles using GIS operations. We utilize such an approach to enrich the metadata of map images by adding spatial extents and place names extracted from map images. We evaluate this GIS-based data augmentation approach by using it to train multiple deep learning models and testing them on two different datasets: a Web Map Service image dataset at the continental scale and an online map image dataset at the state scale. We then discuss the advantages and limitations of the proposed approach.

This paper addresses the finite-time H∞ filtering for a class of nonlinear singular nonhomogeneous Markov jump systems by T-S fuzzy approximation approach, where the transition probabilities (TPs) are time-varying and unknown. First, by considering a stochastic Lyapunov functional and rendering the time-varying TPs inside a polytope, a sufficient condition on singular stochastic H∞ finite-time boundedness (SSH∞FTB) for the filtering error systems is given. Then, by using the matrix inequality decoupling technique, a novel linear matrix inequality (LMI) condition on the existence of the finite-time H∞fuzzy filter is presented. The fuzzy filter is developed in terms of LMIs ensuring the filtering error system is SSH∞FTB. Compared with the previous ones, the proposed design method in this paper has more freedom, leading to less conservative results. A tunnel diode circuit is provided to illustrate the effectiveness and advantage of the design approach proposed in this paper.

It is important for an incremental crawler to know how web pages evolve and the relation between their changing frequencies and the link-attributes such as indegrees. This paper proposes a model for incremental crawling and performs an experiment to verify the correlation between them, by monitoring the evolution of all the link-attributes of the web pages within one website. Particularly, we look deeply into one special kind of page named Index-pages. From the experiment, we can make four conclusions: (1) Pages which have bigger indegrees, outdegrees or PageRank values change more often, and these link-attributes all approximately obey a power-law distribution. (2) The link-attributes of pages seldom change though the pages change themselves. (3) A small proportion of the pages link to most of the vertexes in the web graph. (4) The Index-pages link to sizeable new pages in a website. These conclusions can be used to greatly enhance the performance of an incremental crawler, which is the foremost component for general search engines and web information stores.