X-ray 3D reconstruction has achieved superior performance in medical imaging with traditional and deep learning methods. However, when sparse-view X-ray projections are used to minimize patient exposure to radiation, these methods tend to overfit and produce blurring. To overcome this problem, we propose a novel hybrid feature representation neural attenuation field framework for sparse-view X-ray 3D reconstruction. First, we integrate tri-plane features with hash coding features as the network input, enabling the capture of intricate local details and high-frequency information. Second, we enhance the modeling of radiation attenuation across different organs by designing a specialized attenuation weight estimation network. This network enables the attenuation field estimation network to more accurately focus on the varying attenuation rates of different tissues. Third, we introduce a new multi-skip strategy, which uses the skip connection strategy for each layer of MLPs to the attenuation value and weight prediction network, markedly enhancing the performance of our method. Experiments on public datasets demonstrate the superiority of our proposed method over state-of-the-art methods.

Image Quality Assessment (IQA) is essential for optimizing radiation dose in Computed Tomography (CT) while ensuring radiologists achieve the highest diagnostic accuracy. To advance research in this field, we propose a novel algorithm for CT image quality assessment without the need for reference images. To overcome the challenge of no-reference IQA in CT scans, we employ a novel approach using Poisson Flow Generative models (PFGM) to generate pseudo-reference images for low-dose CT scans. These pseudo-reference images, paired with corresponding low-dose inputs, are fed into a robust regression network specifically designed for this task. To enhance feature extraction, we design a nested convolutional architecture using multi-scale features to improve the feature extraction capability of the regression Network. Furthermore, to strengthen the monotonic correlation between subjective and objective scores, we incorporate the relative distance information within each batch and enforce the relative ranking among the images. To propel research in this domain, we also build and release a new abdominal CT dataset with labeled IQA scores, tailored for benchmarking IQA methods. Extensive qualitative and quantitative experiments, conducted on both public datasets and our newly released dataset, demonstrate that our proposed PFGM-IQA method significantly outperforms state-of-the-art techniques in CT Image Quality Assessment.

Most existing reflection removal methods pay too much attention to the transmission layer and ignore the mutual complementary mechanisms between the transmission layer and the reflection layer. To make full use of the complementarity and distinction between the reflection and transmission layers in reflection-contaminated images, we propose a novel single image reflection removal framework using the feature difference and adaptive information exchange between the transmission and reflection layers of reflection-contaminated images. First, we design a feature difference enhancement module to distinguish and enhance the feature difference of the reflective and transmissive layers. Second, we propose an adaptive information exchange module between transmission and reflection layers in the decoder, which can capture more complementary information. Finally, we introduce a 1/4 selective Instance Normalization strategy to improve our reflection removal tasks further. The experimental results demonstrate the efficiency of the proposed method and superior performance against state-of-the-art methods.

Recently, Graph Neural Networks (GNNs) have made remarkable achievements in semi-supervised classification tasks. Nevertheless, GNNs usually rely on a specific graph convolution which has high computational complexity. To overcome this issue, recent works attempt to implicitly use adjacency matrix to guide message propagation in multi-layer perception (MLP) via neighboring contrastive loss. However, existing works accomplish implicit message passing only, without considering multi-order graph topology information. In this paper, we propose a novel method called Multiple Layer Graph Regularized Perception (MLGP). The main advantage of MLGP is to incorporate multi-order neighboring information into MLP. Further, inspired by gated mechanism, we design a linear gating to capture important features of nodes. More discriminant features can be obtained to alleviate over-smoothing. MLGP is more effective and more robust than existing works when dealing with large-scale graph data and noisy adjacency information. The comparative experiment results show that our model achieves better performance and strong robustness.

Abstract Shadow removal from a single image is an ill‐posed problem because shadow generation is affected by the complex interactions of geometry, albedo, and illumination. Most recent deep learning‐based methods try to directly estimate the mapping between the non‐shadow and shadow image pairs to predict the shadow‐free image. However, they are not very effective for shadow images with complex shadows or messy backgrounds. In this paper, we propose a novel end‐to‐end depth‐aware shadow removal method without using depth images, which estimates depth information from RGB images and leverages the depth feature as guidance to enhance shadow removal and refinement. The proposed framework consists of three components, including depth prediction, shadow removal, and boundary refinement. First, the depth prediction module is used to predict the corresponding depth map of the input shadow image. Then, we propose a new generative adversarial network (GAN) method integrated with depth information to remove shadows in the RGB image. Finally, we propose an effective boundary refinement framework to alleviate the artifact around boundaries after shadow removal by depth cues. We conduct experiments on several public datasets and real‐world shadow images. The experimental results demonstrate the efficiency of the proposed method and superior performance against state‐of‐the‐art methods.