Screen content, which is often computer-generated, has many characteristics distinctly different from conventional camera-captured natural scene content. Such characteristic differences impose majo...

Screen Content Quality Assessment: Overview, Benchmark, and Beyond

Deep learning-based hyperspectral image superresolution methods have achieved great success recently. However, most methods utilize 2D or 3D convolution to explore features, and rarely combine the two types of convolution to design networks. Moreover, when the model only contains 3D convolution, almost all the methods take all the bands of hyperspectral image as input to analyze, which requires more memory footprint. To address these issues, we explore a new structure for hyperspectral image superresolution using spectrum and feature context. Inspired by the high similarity among adjacent bands, we design a dual-channel network through 2D and 3D convolution to jointly exploit the information from both single band and adjacent bands, which is different from previous works. Under the connection of depth split, it can effectively share spatial information so as to improve the learning ability of 2D spatial domain. Besides, our method introduces the features extracted from previous band, which contributes to the complementarity of information and simplifies the network structure. Through feature context fusion, it significantly enhances the performance of the algorithm. Extensive evaluations and comparisons on three public datasets demonstrate that our approach produces the state-of-the-art results over the existing approaches.

Hyperspectral Image Superresolution Using Spectrum and Feature Context

Deep convolutional neural networks have recently made a considerable achievement in the single-image superresolution (SISR) problem. Most CNN architectures for SISR incorporate long or short connections to integrate features, and treat them equally. However, they neglect the discrimination of features, and consequently, achieving relatively poor performance. To address this problem, in this article, we propose a dense discriminative network that is composed of several aggregation modules (AM). Specifically, the AM merges extraction and integration nodes in a tree structure, which can aggregate features progressively in an efficient way. In particular, we compress and rescale the densely connected information in the aggregation node by modeling the interaction between channels, which shares the same insight with the attention mechanism for improving the discriminative ability of network. Extensive experiments conducted on several publicly available datasets have demonstrated the superiority of our model over state-of-the-art in objective metrics and visual impressions.

Image Superresolution via Dense Discriminative Network

With the recent development of drone technology, object detection technology is emerging, and these technologies can also be applied to illegal immigrants, industrial and natural disasters, and missing people and objects. In this paper, we would like to explore ways to increase object detection performance in these situations. Photography was conducted in an environment where it was confusing to detect an object. The experimental data were based on photographs that created various environmental conditions, such as changes in the altitude of the drone, when there was no light, and taking pictures in various conditions. All the data used in the experiment were taken with F11 4K PRO drone and VisDrone dataset. In this study, we propose an improved performance of the original YOLOv5 model. We applied the obtained data to each model: the original YOLOv5 model and the improved YOLOv5_Ours model, to calculate the key indicators. The main indicators are precision, recall, F-1 score, and mAP (0.5), and the YOLOv5_Ours values of mAP (0.5) and function loss were improved by comparing it with the original YOLOv5 model. Finally, the conclusion was drawn based on the data comparing the original YOLOv5 model and the improved YOLOv5_Ours model. As a result of the analysis, we were able to arrive at a conclusion on the best model of object detection under various conditions.

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Improved YOLOv5: Efficient Object Detection Using Drone Images under Various Conditions

Some research progress has been made on fractional-order continuous orthogonal moments (FrCOMs) in the past two years. Compared with integer-order continuous orthogonal moments (InCOMs), FrCOMs increase the number of affine invariants and effectively improve numerical stability. However, the existing types of FrCOMs are still very limited, of which all are planar image oriented. No report on stereoscopic images is available yet. To this end, in this paper, FrCOMs corresponding to various types of InCOMs are first deduced, and then, they are combined with trinion theory to construct trinion FrCOMs (TFrCOMs) applicable to stereoscopic images. Furthermore, the reconstruction performance and geometric invariance of TFrCOMs are analyzed theoretically and experimentally. Finally, an application in the stereoscopic image zero-watermarking algorithm is investigated to verify the superior performance of TFrCOMs. 

Stereoscopic Image Description With Trinion Fractional-Order Continuous Orthogonal Moments

Real-time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector

Bidirectional recurrent autoencoder for 3D skeleton motion data refinement

In this paper, we demonstrate a perceptual-based 3D skeleton motion data refinement method based on a bidirectional recurrent autoencoder, called BRA-P. Three main technical contributions are made by the proposed network. First, the proposed BRA-P can address noisy data with different noise types and amplitudes using one network, and this attribute makes the approach more suitable for raw motion data with heterogeneous mixed noise. Second, due to the usage of perceptual loss, which measures the difference in high-level features extracted by a pretrained perceptual autoencoder, BRA-P improves the perceptual similarity between refined motion data and clean motion data, especially for the case where the noisy data and target clean data have different topologies. Third, BRA-P further improves the bone-length consistency and smoothness of the refined motion using the perceptual autoencoder as a postprocessing network. Ablation experiments verify the effect of the three technical contributions of our approach. The results of the experiments on synthetic noise data and raw motion data captured by Kinect demonstrate that our method outperforms several state-of-the-art methods in the cleaning of mixed-noise data by one network.

/pdf/a-perceptual-based-noise-agnostic-3d-skeleton-motion-data-gg71mj5eu7.pdf

A Perceptual-Based Noise-Agnostic 3D Skeleton Motion Data Refinement Network

Current blind image quality assessment (BIQA) algorithms are mainly designed for natural images. Unfortunately, cartoon and cartoon-like images are quite different from natural images. Hence, recent BIQA methods are not very robust to cartoon images. In this paper, we propose a specific BIQA algorithm designed for cartoon images, which consists of the following terms. First, a cartoon image is divided into edge areas and nonedge areas via a Tchebichef moment (TM)-based process. Second, a multiorder sharpness statistic term is used to measure the quality of the edges, and a sharpness statistic prior model of high-quality (HQ) cartoon images is built. Finally, a local encoding statistic term is adopted to describe the textural complexity in the nonedge areas, and a texture statistic prior model is also established. The experimental results on the cartoon image datasets demonstrate that the proposed method can accurately evaluate the visual quality of cartoon images and is more suitable for cartoon scenarios than some traditional BIQA algorithms.

Blind Quality Assessment for Cartoon Images

In recent years, many deep-network-based super-resolution techniques have been proposed and have achieved impressive results for 2 ${\times }$ and higher magnification factors. However, lower magnification factors encountered in some industrial applications have not received special attention, such as 720P-to-1080P (1.5 ${\times }$ magnification). Compared to traditional 2 ${\times }$ or higher magnification factors, these lower magnifications are much simpler, but reconstructions of high-definition images are time-consuming and computationally complex. Hence, in this paper, a fast image upsampling method is designed specifically for industrial applications at low magnification. In the proposed method, edge and nonedge areas are first distinguished and then reconstructed via different fast approaches. For the edge area, a local edge pattern encoding-based method is presented to recover sharp edges. For the nonedge area, a global iterative reconstruction with texture constraint is utilized. Moreover, some acceleration strategies are also presented to further reduce the complexity. The experimental results demonstrate that the proposed method can obtain performance comparable to that of some state-of-the-art methods for 720P-to-1080P magnification, but the computational cost is much lower.

Shujie Li

Papers

Real-time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector

Bidirectional recurrent autoencoder for 3D skeleton motion data refinement

A Perceptual-Based Noise-Agnostic 3D Skeleton Motion Data Refinement Network

Blind Quality Assessment for Cartoon Images

Are Recent SISR Techniques Suitable for Industrial Applications at Low Magnification