In this paper, an algorithm based on local binary pattern (LBP) is proposed to obtain clear remote sensing images under the premise of unknown causes of blurring. We find that LBP can completely record the texture features of the images, which will not change widely due to the generation of blur. Therefore, LBP prior is proposed, which can filter out the pixels containing important textures in the blurry image through the mapping relationship. The corresponding processing methods are adopted for different types of pixels to cope with the challenges brought by the rich texture and details of remote sensing images and prevent over-sharpening. However, the existence of LBP prior increases the difficulty of solving the model. To solve the model, we construct the projected alternating minimization (PAM) algorithm that involves the construction of the mapping matrix, the fast iterative shrinkage-thresholding algorithm (FISTA) and the half-quadratic splitting method. Experiments with the AID dataset show that the proposed method can achieve highly competitive processing results for remote sensing images.

Infrared dim small target detection is the critical technology in the situational awareness field currently. The detection algorithm of the infrared patch image (IPI) model combined with the total variation term is a recent research hotspot in this field, but there is an obvious staircase effect in target detection, which reduces the detection accuracy to some extent. This paper further investigates the problem of accurate detection of infrared dim small targets and a novel method based on total variation weighted low-rank constraint (TVWLR) is proposed. According to the overlapping edge information of image background structure characteristics, the weights of constraint low-rank items are adaptively determined to effectively suppress the staircase effect and enhance the details. Moreover, an optimization algorithm combined with the augmented Lagrange multiplier method is proposed to solve the established TVWLR model. Finally, the experimental results of multiple sequence images indicate that the proposed algorithm has obvious improvements in detection accuracy, including receiver operating characteristic (ROC) curve, background suppression factor (BSF) and signal-to-clutter ratio gain (SCRG). Furthermore, the proposed method has stronger robustness under complex background conditions such as buildings and trees.

Dynamic analysis of the optical–mechanical structure based on the linear state space expression is performed for a three-point flexural mount lightweight space mirror with a diameter of ϕ740 mm. Using linear structure dynamics and linear state space theories, the state space model of the mirror assembly is established based on modal information. The DC gain method is used to reduce modes and a frequency response analysis of the reduced modes is performed to obtain the frequency domain transfer function between the excitation input and response output points and determine the contribution of each mode to the total frequency response. The frequency response curve is plotted. A mechanical vibration test is performed to verify the accuracy and rationality of simulation analysis. The dynamic analysis method based on state space theory provides a new method of investigating optical and mechanical structures, which can help efficiently and accurately analyze the frequency response characteristics of complex linear systems.

The increasing popularity of video streaming services and the widespread accessibility of high-speed internet underscore the importance of delivering cost-effective and seamless streaming experiences. Shared internet connections may lead to varying speeds, impacting Quality of Experience (QoE). Rate adaptation techniques aim to ensure smooth video transmission, but overly optimistic adaptations can compromise user experience. Objective video quality assessment is crucial for efficient rate adaptation to ensure smooth QoE. This research proposes a novel method incorporating temporal channel shifting into Convolutional Neural Networks (CNN) for video quality assessment while maintaining the computational simplicity of a 2D CNN model. The proposed approach relies on the EfficientNet architecture, initially pre-trained on quality-aware images, and fine-tune it using datasets of rate-adaptive videos. The model is trained and evaluated on two benchmark datasets, namely "Waterloo sQoE III" and "LIVE Netflix II," which consist of rate-adaptive videos annotated with subjective quality scores. Experimental results encompass the evaluation of Pearson, Spearman, and Kendall correlation coefficients, along with the computation time ratio for the proposed approach. The outcomes reveal competitive scores of 0.795, 0.652, 0.772, and 0.216 for the "Live Netflix II dataset" and 0.782, 0.713, 0.721, and 0.230 for the "Waterloo sQoE III dataset." Our proposed method, compared to 24 approaches for "Waterloo sQoE III" and 25 for "LIVE Netflix II," attains the highest correlation scores while maintaining near-real-time processing efficiency. These results affirm the efficacy of our approach in accurately predicting human judgment (QoE) with computational efficiency.

Detecting faint and diminutive infrared targets devoid of clearly defined shape and texture details in intricate surroundings remains a formidable challenge within the domain of target detection. Current methodologies employing deep neural networks and pooling operations can easily cause small target loss, resulting in suboptimal detection outcomes. To address these issues, we design an innovative dynamic context-aware pyramid network (DCAPNet). It comprises three core modules: dynamic context modulation (DCM), dynamic pyramid context (DPC), and shuffle attention fusion (SAF). Specifically, the DCM module is designed to adaptively capture diverse-scale information from input images, adapting to various target dimensions and enhancing the feature representation capabilities crucial for effective target detection. Subsequently, the DPC module adaptively captures multi-scale features and better utilizes contextual information by aggregating multiple DCM modules. This facilitates the retention of essential semantic information about small infrared targets within deeper network layers. Finally, through the designed SAF module, we facilitate the exchange of information within the same layer and establish correlations between different layers, ensuring the fusion of shallow spatial positional information and deep semantic information to enhance the overall detection performance. Furthermore, comprehensive ablation studies are conducted to substantiate the efficacy of the designed modules within the proposed network architecture. Simultaneously, we conducted a comparative analysis of the proposed network algorithm against several state-of-the-art methodologies for infrared small target detection, employing multiple evaluation metrics. The results consistently demonstrated the proposed model attains superior detection performance on the publicly available IRSTD-1k, SIRST-Aug and NUDT-SIRST datasets.

The remote sensing imaging environment is complex, in which many factors cause image blur. Thus, without prior knowledge, the restoration model established to obtain clear images can only rely on the observed blurry images. We still build the prior with extreme pixels but no longer traverse all pixels, such as the extreme channels. The features are extracted in units of patches, which are segmented from an image and partially overlap with each other. In this paper, we design a new prior, i.e., overlapped patches’ non-linear (OPNL) prior, derived from the ratio of extreme pixels affected by blurring in patches. The analysis of more than 5000 remote sensing images confirms that OPNL prior prefers clear images rather than blurry images in the restoration process. The complexity of the optimization problem is increased due to the introduction of OPNL prior, which makes it impossible to solve it directly. A related solving algorithm is established based on the projected alternating minimization (PAM) algorithm combined with the half-quadratic splitting method, the fast iterative shrinkage-thresholding algorithm (FISTA), fast Fourier transform (FFT), etc. Numerous experiments prove that this algorithm has excellent stability and effectiveness and has obtained competitive processing results in restoring remote sensing images.