Deep learning-based methods have dominated the field of image denoising with their superior performance. Most of them belong to the non-blind denoising approaches assuming that the noise is known at a specific level. However, real-world noise is complex and usually unknown. Since the distribution and level of noise are often unavailable, it will lead to severe performance degradation for non-blind denoising methods. Therefore, introducing noise levels is crucial for the challenging real-world denoising problem. Meanwhile, we observe that noise level mismatch will bring some artifacts to the denoised images. An intuitive solution is using the intermediate denoised images to correct the inaccurate noise level maps. Thus, we introduce an iterative correction scheme, yielding better results than direct noise prediction. We further propose an effective guided feature domain denoising residual network that can promote denoising for various real-world noises using iteratively denoised features, initial image features, and noise level maps. Experimental results on real-world image datasets show that the proposed method can provide excellent visual and objective performance for the real-world denoising task.

Cross-modality person re-identification between the visible domain and infrared domain is important but extremely challenging for night-time surveillance. Besides the cross-modality discrepancies caused by different camera spectrums, visible infrared person re-identification(VI-REID) still suffers from much pedestrian misalignment as well as the variations caused by different camera viewpoints and various pedestrian pose deformations like traditional person re-identification. In this paper, we propose a multi-path adaptive pedestrian alignment network(MAPAN) to learn discriminative feature representations. The multi-path network learns features directly from the data in an end-to-end manner and aligns the pedestrians adaptively without any additional manual annotations. To alleviate the intra-modality discrepancies caused by image misalignment, we combine the aligned visible image features with the original visible image features and enhance the attention of the network towards pedestrians, resulting in significant improvements in distinguishability of the learning features. To mitigate the cross-modality discrepancies between the visible domain and the infrared domain, the discriminative features of the two modalities are mapped to the same feature embedding space, and the identity loss as well as triplet loss is incorporated as the overall loss. Extensive experiments demonstrate the superior performance of proposed method compared to the state-of-the-arts.

Abnormal crowd behavior detection is an advanced topic researched in fields of computer vision and digital image processing. The problems such as diversity of monitoring scene, different crowd density and mutual occlusion among crowds etc result in a low recognition rate for abnormal crowd behavior detection. In order to solve these problems, this paper combines a streakline model based on fluid dynamics with an abnormal behavior detection method presented by Hassner et al., and proposes a modified algorithm to improve the recognition accuracy of abnormal crowd behavior. Finally, the validity and accuracy of the algorithm are verified via a large amount of challenging real-world surveillance videos.

Tree‐structured compressive sensing (CS) shows that it is possible to recover tree‐sparse signals using fewer measurements compared with conventional CS. However, performance guarantees rely heavily on the premise that an exact tree projection (ETP) algorithm is employed. Nevertheless, for a given sparsity, the condensing sort and select algorithm in the model‐based compressive sampling matching pursuit (CoSaMP) algorithm can only yield an approximate tree projection. Therefore, in order to ensure reconstruction precision, the authors propose the combination of an ETP algorithm with the CoSaMP algorithm. Further, the hierarchical wavelet connected tree is also integrated into the ETP‐CoSaMP algorithm to offset the high computational complexity of the ETP algorithm. Experimental results indicate that the hierarchical ETP based on CoSaMP algorithm (HETP‐CoSaMP algorithm) enhances reconstruction accuracy while retaining reconstruction time that is comparable with that of the model‐based CoSaMP algorithm.

Reconstruction of microstructure in granular porous media, which can be viewed as granular assemblies, is crucial for studying their characteristics and physical properties in various fields concerned with the behavior of such media, including petroleum geology and computational materials science. In spite of the fact that many existing studies have investigated grain reconstruction, most of them treat grains as simplified individuals for discrete reconstruction, which cannot replicate the complex geometrical shapes and natural interactions between grains. In this work, a hybrid generative model based on a deep-learning algorithm is proposed for high-quality three-dimensional (3D) microstructure reconstruction of granular porous media from a single two-dimensional (2D) slice image. The method extracts 2D prior information from the given image and generates the grain set as a whole. Both a self-attention module and effective pattern loss are introduced in a bid to enhance the reconstruction ability of the model. Samples with grains of varied geometrical shapes are utilized for the validation of our method, and experimental results demonstrate that our proposed approach can accurately reproduce the complex morphology and spatial distribution of grains without any artificiality. Furthermore, once the model training is complete, rapid end-to-end generation of diverse 3D realizations from a single 2D image can be achieved.