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.