Edge-preserving image smoothing plays a vital role in the field of computational imaging. It is a valuable technique that has applications in various tasks. However, different tasks have specific requirements for edge preservation. Existing filters do not take into account the task-dependent smoothing behavior, resulting in visually distracting artifacts. We propose a flexible edge-preserving image filter based on a nonconvex Welsch penalty. Compared with the convex models, our model can better handle complex data and capture nonlinear relationships, thus providing better results. We combine deep unsupervised learning and graduated nonconvexity to solve our nonconvex objective function, where the main network structure is designed as a Swin transformer complemented with the locally enhanced feed-forward network. Experimental results show that the proposed method achieves excellent performance in various applications, including image smoothing, high dynamic range tone mapping, detail enhancement, and edge extraction.

Crowd counting aims to derive information about crowd density by quantifying the number of individuals in an image or video. It offers crucial insights applicable to various domains, e.g., secure, efficient decision-making, and management. However, scale variation and irregular shapes of heads pose intricate challenges. To address these challenges, we propose a scale-deformation awareness network (SDANet). Specifically, a scale awareness module is introduced to address the scale variation. It can capture long-distance dependencies and preserve precise spatial information by readjusting weights in height and width directions. Concurrently, a deformation awareness module is introduced to solve the challenge of head deformation. It adjusts the sampling position of the convolution kernel through deformable convolution and learning offset. Experimental results on four crowd-counting datasets prove the superiority of SDANet in accuracy, efficiency, and robustness.

Real-world image denoising is a critical task in image processing, aiming to restore clean images from their noisy counterparts captured in natural environments. While diffusion models have demonstrated remarkable success in image generation, surpassing traditional generative models, their application to image denoising has been limited due to challenges in controlling noise generation effectively. We present a general denoising method inspired by diffusion models. Specifically, our approach employs a diffusion process with linear interpolation, enabling control of noise generation. By interpolating the intermediate noisy image between the original clean image and the corresponding real-world noisy one, our model is able to achieve controllable noise generation. Moreover, we introduce two sampling algorithms for this diffusion model: a straightforward procedure aligned with the diffusion process and an enhanced version that addresses the shortcomings of the former. Experimental results demonstrate that our proposed method, utilizing simple convolutional neural networks such as UNet, achieves denoising performance comparable to that of the transformer architecture.

The most common form of image forgery is copy-move, which arises when an image region is duplicated and pasted onto another region of the same image. An effective algorithm for copy-move forgery detection based on binarized statistical image features (BSIF) and principal component analysis (PCA) is presented. Initially, the suspicious image is converted to grayscale and is subsequently partitioned into overlapping blocks. Feature vectors are extracted from these blocks using BSIF, followed by dimensionality reduction using PCA. Next, as a precursor to the matching step, the feature vectors are sorted lexicographically. Additionally, a morphological opening operation is applied to eliminate outliers. This algorithm offers not just forgery detection but also the ability to localize and identify duplicated regions. The proposed algorithm was assessed using three datasets: CoMoFoD, GRIP, and UNIPA. The experimental results show that this algorithm is fast and has high accuracy for forgery detection and localization. Moreover, it has high robustness under various postprocessing operations, such as brightness, contrast adjustments, and blurring. Furthermore, the proposed algorithm outperforms some recent approaches in overall performance.

Recognizing three-dimensional (3D) objects based on local feature descriptors is a highly challenging task. Existing 3D local feature descriptors rely on single-scale surface normals, which are susceptible to noise and outliers, significantly compromising their effectiveness and robustness. A multi-scale point pair normal encoding (M-POE) method for 3D object recognition is proposed. First, we introduce the M-POE descriptor, which encodes voxelized features with multi-scale normals to describe local surfaces, exhibiting strong distinctiveness and robustness against various interferences. Second, we present guided sample consensus in second-order graphs (GSAC-SOG), an extension of RANSAC that incorporates geometric constraints and reduces sampling randomness, enabling accurate estimation of the object’s six-degree-of-freedom (6-DOF) pose. Finally, a 3D object recognition method based on the M-POE descriptor is proposed. The proposed method is evaluated on five standard datasets with state-of-the-art comparisons. The results demonstrate that (1) M-POE is robust, discriminative, and efficient; (2) GSAC-SOG is robust to outliers; (3) the proposed 3D object recognition method achieves high accuracy and robustness against clutter and occlusion, with recognition rates of 99.45%, 94.21%, and 97.88% on the U3OR, Queen, and CFV datasets, respectively.

The utilization of chest X-ray (CXR) image data analysis for assisting in disease diagnosis is an important application of artificial intelligence. Supervised learning faces challenges due to a lack of large-scale labeled datasets and inaccuracies. Self-supervised learning offers a potential solution, but current research in this area is limited, and the diagnostic accuracy remains unsatisfactory. We propose an approach that integrates the self-supervised Bidirectional Encoder Representations from Image Transformers version 2 (BEiTv2) method with the vector quantization-based knowledge distillation (VQ-KD) strategy into CXR image data to enhance disease diagnosis accuracy. Our methodology demonstrates superior performance compared with existing self-supervised methods, showcasing its efficacy in improving diagnostic outcomes. Through transfer and ablation studies, we elucidate the benefits of the VQ-KD strategy in enhancing model performance and transferability to downstream tasks.

Video hashing is an efficient technique for tasks like copy detection and retrieval. This paper utilizes canonical polyadic (CP) decomposition and Hahn moments to design a robust video hashing. The first significant contribution is the secondary frame construction. It uses three weighted techniques to generate three secondary frames for each video group, which can effectively capture features of video frames from different aspects and thus improves discrimination. Another contribution is the deep feature extraction via the ResNet50 and CP decomposition. The use of the ResNet50 can provide rich features and the CP decomposition can learn a compact and discriminative representation from the rich features. In addition, the Hahn moments of secondary frames are taken to construct hash elements. Extensive experiments on the open video dataset demonstrate that the proposed algorithm surpasses several state-of-the-art algorithms in balancing discrimination and robustness.

In the current distributed environment, reversible data hiding in encrypted images has the disadvantages of low security and nonprogressivity. To address this problem, a homomorphic embedding algorithm is proposed based on polynomial secret sharing (PSS) and Chinese remainder theorem. First, the image owner encrypts the carrier image in streaming encryption and sends it to the data hider. Then, the data hider utilizes PSS to split the carrier image into n shares. At the same time, extra secrets after SS are embedded into the carrier shares using homomorphism. After splitting by Chinese remainder theorem, every share of the embedded data is divided into some sub-shares and then distributed to the participants. The participants that satisfy the threshold condition provide part or all of the sub-shares according to the authority of the data extractor. If each participant provides all sub-shares, the secrets and carrier image can be reconstructed completely. If each participant provides part of the sub-shares, the secrets and carrier image can be reconstructed partly. The experimental results show that the proposed scheme has progressivity, high security, and a large embedding rate (ER). Meanwhile, the ER is not affected by the carrier image.

The smooth regions in depth videos contain a significant proportion of homogeneous content, resulting in many spatial redundancies. To improve the coding efficiency of depth videos, this paper proposes a deep degradation-aware up-sampling-based depth video coding method. For reducing spatial redundancies effectively, the proposed method compresses the depth video at a low resolution, and restores the resolution by utilizing the learning-based up-sampling technology. To recover high-quality depth videos, a degradation-aware up-sampling network is proposed, which explores the degradation information of compression artifacts and sampling artifacts to restore the resolution. Specifically, the compression artifact removal module is used to obtain refined low-resolution depth frames by learning the representation of compression artifacts. Meanwhile, a jointly optimized learning strategy is designed to enhance the capability of recovering high-frequency details, which is beneficial for up-sampling. According to the experimental results, the proposed method achieves considerable performance in depth video coding compared with 3D-HEVC.

Radar imaging techniques, such as synthetic aperture radar, are widely explored in automatic vehicle recognition algorithms for remote sensing tasks. A large basis of literature covering several machine learning methodologies using visual information transformers, self-attention, convolutional neural networks (CNN), long short-term memory (LSTM), CNN-LSTM, CNN-attention-LSTM, and CNN Bi-LSTM models for detection of military vehicles have been attributed with high performance using a combination of these approaches. Tradeoffs between differing number of poses, single/multiple feature extraction streams, use of signals and/or images, as well as the specific mechanisms used to combine them, have widely been debated. We propose the adaptation of several models towards a unique biologically inspired architecture that utilizes both multi-pose and multi-contextual image and signal radar sensor information to make vehicle assessments over time. We implement a compact multi-pose 3D CNN single stream to process and fuse multi-temporal images while a dual sister 2D CNN stream processes the same information over a lower-dimensional power-spectral domain to mimic the way multi-sequence visual imagery is combined with auditory feedback for enhanced situational awareness. These data are then fused across data domains using transformer-modified encoding blocks to Bi-LSTM segments. Classification results on a fundamentally controlled simulated dataset yielded accuracies of up to 98% and 99% in line with literature. This enhanced performance was then evaluated for robustness not previously explored for three simultaneous parameterizations of incidence angle, object orientation, and lowered signal-to-noise ratio values and found to increase recognition on all three cases for low to moderate noised environments.

In recent years, underwater target detection algorithms based on deep learning have greatly promoted the development of the field of marine science and underwater robotics. However, due to the complexity of the underwater environment, there are problems, such as target occlusion, overlap, background confusion, and small object, that lead to detection difficulties. To address this issue, this paper proposes an improved underwater target detection method based on YOLOv8s. First, a lightweight backbone network with efficient transformers is used to replace the original backbone network, which enhances the contextual feature extraction capability. Second, an improved bidirectional feature pyramid network is used in the later multi-scale fusion part by increasing the input of bottom-level information while reducing the model size and number of parameters. Finally, a dynamic head with an attention mechanism is introduced into the detection head to enhance the classification and localization of small and fuzzy targets. Experimental results show that the proposed method improves the mAP0.5:0.95 of 65.7%, 63.7%, and 51.2% with YOLOv8s to that of 69.2%, 66.8%, and 54.8%, on three public underwater datasets, DUO, RUOD, and URPC2020, respectively. Additionally, compared with the YOLOv8s model, the model size decreased from 21.46 to 15.56 MB, and the number of parameters decreased from 11.1 to 7.9 M.