Radio Frequency Identification (RFID) is a core technology in the perception layer of the Passive Internet of Things (Passive IoT). The complex external environments and an increase in the number of tags would cause channel contention and data conflicts during the reading process, which significantly affects the positioning accuracy and reading performance, resulting in the loss of data information stored in the tags. Although there are many methods to improve the reading performance of RFID system, most of them evaluate the reading performance through anti-collision protocol to optimize the position of reader and antenna. However, when moving antennas and readers read multi-tag, the solutions are neither efficient nor reliable. To improve the performance of RFID systems, this paper proposes an improved multi-tag position measuremnet for UNet networks, which can optimize the reading performance of RFID system. Firstly, for the electronic interference in complex environments, an experimental platform assisted visual intelligence is designed to construct the RFID multi-tag position measurement and performance analysis system. Secondly, the image deblurring for the UNet network improved by Implicit Neural Representations (INR) and Residual Fast Fourier Transform (Res FFT) is proposed to improve the image quality of the degraded multi-tag image. Finally, the 3D coordinates of the tags are found by YOLOv9 in the image coordinate system, which are subsequently converted to actual 3D distributions. It can improve the spatial distribution of RFID tags to Combine the prior knowledge of RFID 3D space in visual intelligence with the indicators of reading performance, thereby better guiding more effective physical tags placement methods. Experimental results demonstrate that the PSNR of the proposed method is 30.17 dB, and at least 2% better than the state-of-the-art algorithms, which indicates that the method proposed can accurately obtain the 3D distribution of multi-tag. Our system can capture the multi-tag 3D distribution corresponding to the maximum reading distance, thereby guiding the 3D structure distribution of multi-tag to enhance RFID reading performance.

In autonomous driving, LiDAR sensors are vital for acquiring 3D point clouds, providing reliable geometric information. However, traditional sampling methods of preprocessing often ignore semantic features, leading to detail loss and ground point interference in 3D object detection. To address this, we propose a multi-branch two-stage 3D object detection framework using a Semantic-aware Multi-branch Sampling (SMS) module and multi-view consistency constraints. The SMS module includes random sampling, Density Equalization Sampling (DES) for enhancing distant objects, and Ground Abandonment Sampling (GAS) to focus on non-ground points. The sampled multi-view points are processed through a Consistent KeyPoint Selection (CKPS) module to generate consistent keypoint masks for efficient proposal sampling. The first-stage detector uses multi-branch parallel learning with multi-view consistency loss for feature aggregation, while the second-stage detector fuses multi-view data through a Multi-View Fusion Pooling (MVFP) module to precisely predict 3D objects. The experimental results on the KITTI dataset and Waymo Open Dataset show that our method achieves excellent detection performance improvement for a variety of backbones, especially for low-performance backbones with simple network structures. The code will be publicly available at https://github.com/HaoJing-SX/SMS.

Inspired by multi-task learning, degraded low-quality color-depth images enhancement tasks are transformed as a joint color-depth optimization model by using maximum a posteriori estimation. This model is optimized alternatively in an iterative way to get the solutions of CGD-SR task and Low-Brightness Color Image Enhancement (LBC-IE) task. The whole iterative optimization procedure is expanded as a joint model-driven unfolding network. Many experimental results have confirmed that high-resolution reconstruction of the depth map and the enhancement of low-brightness image can be realized simultaneously in one network. Furthermore, the proposed method with network interpretability can exceed that of many inexplicable CGD-SR methods and LBC-IE methods.

Although color-guided depth map super-resolution methods based on deep learning have achieved great progress, these methods are not explainable and their super-resolution results are suffered from texture-copying and boundary blurring problems. In order to alleviate these problems, we propose an explainable depth map super-resolution network by unfolding edge-constrained optimization model, dubbed Ex-DSRNet. First, we propose an edge reconstruction network to recover accurate edge information, then feed edge information and color information into different depth map reconstruction networks to reconstruct depth information. Finally, a high-fidelity network is proposed to fuse the above two kinds of depth information to obtain high-quality depth map. A large number of experimental results have demonstrated that the proposed Ex-DSRNet can compete against many state-of-the-art depth map super-resolution methods in term of root mean square error.

In this paper, we propose a deep multiple description coding framework, whose quantizers are adaptively learned via the minimization of multiple description compressive loss. Firstly, our framework is built upon auto-encoder networks, which have multiple description multi-scale dilated encoder network and multiple description decoder networks. Secondly, two entropy estimation networks are learned to estimate the informative amounts of the quantized tensors, which can further supervise the learning of multiple description encoder network to represent the input image delicately. Thirdly, a pair of scalar quantizers accompanied by two importance-indicator maps is automatically learned in an end-to-end self-supervised way. Finally, multiple description structural dissimilarity distance loss is imposed on multiple description decoded images in pixel domain for diversified multiple description generations rather than on feature tensors in feature domain, in addition to multiple description reconstruction loss. Through testing on two commonly used datasets, it is verified that our method is beyond several state-of-the-art multiple description coding approaches in terms of coding efficiency.

Multiple description coding (MDC) is able to stably transmit signal in un-reliable and non-prioritized networks, which has been broadly studied for several decades. However, traditional MDC does not well leverage image's context features to generate multiple descriptions. In this paper, we propose a novel standard-compliant convolutional neural network-based MDC framework, which efficiently leverages image's context information to compress the image. First, multiple description generator network (MDGN) is designed to produce appearance-similar yet feature-different multiple descriptions automatically according to image's content, which are compressed by a standard codec. Second, we present multiple description reconstruction network (MDRN) including side reconstruction networks (SRNs) and central reconstruction network (CRN). When any one of two lossy descriptions is received at decoder, SRN network is used to improve the quality of this decoded lossy description by simultaneously removing compression artifact and up-sampling. Meanwhile, we utilize CRN network with two decoded descriptions as inputs for better reconstruction, if both of lossy descriptions are available. Third, multiple description virtual codec network is proposed to bridge the gap between MDGN network and MDRN network in order to train an end-to-end MDC framework. Here, two learning algorithms are provided to train our whole framework. In addition to structural dis-similarity loss function, the produced descriptions are used as opposing labels with multiple description distance loss function to regularize the training of MDGN network. These losses guarantee that the generated descriptions are structurally similar yet finely diverse. Experimental results show a great deal of objective and subjective quality measurements to validate the effectiveness of our framework.

In 3D video coding and depth-based image rendering, the distortion of the compressed depth image often leads to wrong 3D warpping. In this paper, by generalizing the recent work of convolutional neural network (CNN)-based depth image up-sampling, we propose a CNN-based depth image artifact removal scheme, where both the compressed depth and color images are used to enhance the depth accuracy. The proposed CNN has two sub-networks: joint depth-color sub-network and joint depth sub-network. During the depth and color feature extraction, the gradient of the depth image is used as the input to color image, while the gradient of color image is used as the input of depth feature extraction. Such an exchange of gradient information improves the learned features. Experimental results in terms of both objective and subjective quality of the depth and color images verify the efficiency of the proposed method.

Learning-based image super-resolution methods often use large datasets to learn texture features. When these methods are applied to depth images, emphasis should be given on learning the geometrical structures at object boundaries, since depth images do not have much texture information. In this paper, we develop a scheme to learn multiple residual dictionaries from only one external image. After depth image super-resolution, some artifacts may appear. An adaptive depth map refinement method is then proposed to remove these artifacts along the depth edges, based on the shape-adaptive weighted median filtering method. Experimental results demonstrate the advantage of the proposed method over many other methods.

In the general 3D scene, the correlation of depth image and corresponding color image exists, so many filtering methods have been proposed to improve the quality of depth images according to this correlation. Unlike the conventional methods, in this paper both depth and color information can be jointly employed to improve the quality of compressed depth image by the way of iterative guidance. Firstly, due to noises and blurring in the compressed image, a depth pre-filtering method is essential to remove artifact noises. Considering that the received geometry structure in the distorted depth image is more reliable than its color image, the color information is merged with depth image to get depth-merged color image. Then the depth image and its corresponding depth-merged color image can be used to refine the quality of the distorted depth image using joint iterative guidance filtering method. Therefore, the efficient depth structural information included in the distorted depth images are preserved relying on depth itself, while the corresponding color structural information are employed to improve the quality of depth image. We demonstrate the efficiency of the proposed filtering method by comparing objective and visual quality of the synthesized image with many existing depth filtering methods.