A Super-Resolution Reconstruction Driven Helmet Detection Workflow

The degrading of input images due to the engineering environment decreases the performance of helmet detection models so as to prevent their application in practice. To overcome this problem, we propose an end-to-end helmet monitoring system, which implements a super-resolution (SR) reconstruction driven helmet detection workflow to detect helmets for monitoring tasks. The monitoring system consists of two modules, the super-resolution reconstruction module and the detection module. The former implements the SR algorithm to produce high-resolution images, the latter performs the helmet detection. Validations are performed on both a public dataset as well as the realistic dataset obtained from a practical construction site. The results show that the proposed system achieves a promising performance and surpasses the competing methods. It will be a promising tool for construction monitoring and is easy to be extended to corresponding tasks.

Pyramid Information Distillation Attention Network for Super-Resolution Reconstruction of Remote Sensing Images

Image super-resolution (SR) technology aims to recover high-resolution images from low-resolution originals, and it is of great significance for the high-quality interpretation of remote sensing images. However, most present SR-reconstruction approaches suffer from network training difficulties and the challenge of increasing computational complexity with increasing numbers of network layers. This indicates that these approaches are not suitable for application scenarios with limited computing resources. Furthermore, the complex spatial distributions and rich details of remote sensing images increase the difficulty of their reconstruction. In this paper, we propose the pyramid information distillation attention network (PIDAN) to solve these issues. Specifically, we propose the pyramid information distillation attention block (PIDAB), which has been developed as a building block in the PIDAN. The key components of the PIDAB are the pyramid information distillation (PID) module and the hybrid attention mechanism (HAM) module. Firstly, the PID module uses feature distillation with parallel multi-receptive field convolutions to extract short- and long-path feature information, which allows the network to obtain more non-redundant image features. Then, the HAM module enhances the sensitivity of the network to high-frequency image information. Extensive validation experiments show that when compared with other advanced CNN-based approaches, the PIDAN achieves a better balance between image SR performance and model size.

Parameter Analysis on Backprojection for Super-Resolution (SR) Reconstruction Algorithm

In this paper, an image SR reconstruction scheme by using k-Singular Value Decomposition (k-SVD) with Orthogonal Matching Pursuit (OMP) as sparse coding method was proposed to obtain the High-Resolution (HR) image. The system conducted in this paper consists of two parts: image SR reconstruction algorithm and also the backprojection process. Since this paper is focused on analysing the effect of parameters in backprojection on the performance of final images produced at the end of the process, therefore, the flow of backprojection is discussed. Generally, the backprojection algorithm is added to the approach to improve the image quality by sharpening the edges of the HR image. However, the parameters used in backprojection could be the factor that caused decrease in the image quality. Thus, the main idea of this paper is to analyse these parameters throughout the flow of backprojection algorithm. The parameters include the predefined 2D filter and interpolation method. Based on the results, it can be concluded that the use of averaging filter (hsize = 3), Gaussian filter (hsize = 5, 7 and 9; σ = 1) and bicubic interpolation method or also known as the cubic kernel can be adopted to the backprojection algorithm since these parameters achieved the highest RMSE, PSNR and SSIM values of 13.87, 25.29dB and 0.83 respectively. The analyses done in this paper has brought a clear understanding on the backprojection algorithm and further implementation and analysis in backprojection such as by using cascaded filters for averaging and Gaussian filters can be considered in the future.

Super-resolution PIV using multi-frame displacement information

High-resolution (HR) fluid-flow velocity information is important to reliably analyze fluid measurements in particle image velocimetry (PIV), such as the boundary layer and turbulent flow. Efforts in PIV to enhance the resolution of flow fields are mainly based on single-frame information, which follows the velocity field estimation and may influence the final reconstruction accuracy. In this study, we propose a novel super-resolution (SR) reconstruction technology from another perspective, which consists of two parts: a multi-frame imaging system and a Bayesian-based multi-frame SR reconstruction algorithm. First, a splitbased imaging system is developed to obtain particle image pairs with fixed displacements. Subsequently, we present a Bayesian-based multi-frame SR (BMFSR) reconstruction algorithm to obtain an SR particle image. Multi-frame particle images collected by the developed system are used as the input low-resolution images for the following novel SR reconstruction algorithm. Synthetic and experimental particle images have been tested to verify the performance of the proposed technology, and the results are compared with the traditional and advanced reconstruction methods in PIV. The results and comparisons show that the proposed technology successfully achieves good performance in obtaining finer particle images and a more accurate velocity field.

Deep Residual Dual-Attention Network for Super-Resolution Reconstruction of Remote Sensing Images

A super-resolution (SR) reconstruction of remote sensing images is becoming a highly active area of research. With increasing upscaling factors, richer and more abundant details can progressively be obtained. However, in comparison with natural images, the complex spatial distribution of remote sensing data increases the difficulty in its reconstruction. Furthermore, most SR reconstruction methods suffer from low feature information utilization and equal processing of all spatial regions of an image. To improve the performance of SR reconstruction of remote sensing images, this paper proposes a deep convolutional neural network (DCNN)-based approach, named the deep residual dual-attention network (DRDAN), which achieves the fusion of global and local information. Specifically, we have developed a residual dual-attention block (RDAB) as a building block in DRDAN. In the RDAB, we firstly use the local multi-level fusion module to fully extract and deeply fuse the features of the different convolution layers. This module can facilitate the flow of information in the network. After this, a dual-attention mechanism (DAM), which includes both a channel attention mechanism and a spatial attention mechanism, enables the network to adaptively allocate more attention to regions carrying high-frequency information. Extensive experiments indicate that the DRDAN outperforms other comparable DCNN-based approaches in both objective evaluation indexes and subjective visual quality.

Edge-Enhanced with Feedback Attention Network for Image Super-Resolution

Significant progress has been made in single image super-resolution (SISR) based on deep convolutional neural networks (CNNs). The attention mechanism can capture important features well, and the feedback mechanism can realize the fine-tuning of the output to the input. However, they have not been reasonably applied in the existing deep learning-based SISR methods. Additionally, the results of the existing methods still have serious artifacts and edge blurring. To address these issues, we proposed an Edge-enhanced with Feedback Attention Network for image super-resolution (EFANSR), which comprises three parts. The first part is an SR reconstruction network, which adaptively learns the features of different inputs by integrating channel attention and spatial attention blocks to achieve full utilization of the features. We also introduced feedback mechanism to feed high-level information back to the input and fine-tune the input in the dense spatial and channel attention block. The second part is the edge enhancement network, which obtains a sharp edge through adaptive edge enhancement processing on the output of the first SR network. The final part merges the outputs of the first two parts to obtain the final edge-enhanced SR image. Experimental results show that our method achieves performance comparable to the state-of-the-art methods with lower complexity.

Video Super-Resolution Based on Generative Adversarial Network and Edge Enhancement

With the help of deep neural networks, video super-resolution (VSR) has made a huge breakthrough. However, these deep learning-based methods are rarely used in specific situations. In addition, training sets may not be suitable because many methods only assume that under ideal circumstances, low-resolution (LR) datasets are downgraded from high-resolution (HR) datasets in a fixed manner. In this paper, we proposed a model based on Generative Adversarial Network (GAN) and edge enhancement to perform super-resolution (SR) reconstruction for LR and blur videos, such as closed-circuit television (CCTV). The adversarial loss allows discriminators to be trained to distinguish between SR frames and ground truth (GT) frames, which is helpful to produce realistic and highly detailed results. The edge enhancement function uses the Laplacian edge module to perform edge enhancement on the intermediate result, which helps further improve the final results. In addition, we add the perceptual loss to the loss function to obtain a higher visual experience. At the same time, we also tried training network on different datasets. A large number of experiments show that our method has advantages in the Vid4 dataset and other LR videos.

Perceptual Metric Guided Deep Attention Network for Single Image Super-Resolution

Deep learning has been widely applied to image super-resolution (SR) tasks and has achieved superior performance over traditional methods due to its excellent feature learning capabilities. However, most of these deep learning-based methods require training image sets to pre-train SR network parameters. In this paper, we propose a new single image SR network without the need of any pre-training. The proposed network is optimized to achieve the SR reconstruction only from a low resolution observation rather than training image sets, and it focuses on improving the visual quality of reconstructed images. Specifically, we designed an attention-based decoder-encoder network for predicting the SR reconstruction, in which a residual spatial attention (RSA) unit is deployed in each layer of decoder to capture key information. Moreover, we adopt the perceptual metric consisting of L1 metric and multi-scale structural similarity (MSSSIM) metric to learn the network parameters. Different than the conventional MSE (mean squared error) metric, the perceptual metric coincides well with perceptual characteristics of the human visual system. Under the guidance of the perceptual metric, the RSA units are capable of predicting the visually sensitive areas at different scales. The proposed network can thus pay more attention to these areas for preserving visual informative structures at multiple scales. The experimental results on the Set5 and Set14 image set demonstrate that the combination of Perceptual metric and RSA units can significantly improve the reconstruction quality. In terms of PSNR and structural similarity (SSIM) values, the proposed method achieves better reconstruction results than the related works, and it is even comparable to some pre-trained networks.

Depth image super-resolution reconstruction based on a modified joint trilateral filter

Depth image super-resolution (SR) is a technique that uses signal processing technology to enhance the resolution of a low-resolution (LR) depth image. Generally, external database or high-resolution (HR) images are needed to acquire prior information for SR reconstruction. To overcome the limitations, a depth image SR method without reference to any external images is proposed. In this paper, a high-quality edge map is first constructed using a sparse coding method, which uses a dictionary learned from the original images at different scales. Then, the high-quality edge map is used to guide the interpolation for depth images by a modified joint trilateral filter. During the interpolation, some information of gradient and structural similarity (SSIM) are added to preserve the detailed information and suppress the noise. The proposed method can not only preserve the sharpness of image edge, but also avoid the dependence on database. Experimental results show that the proposed method is superior to some state-of-the-art depth image SR methods.