Real-world imagery does not always exhibit good visibility and clean content, but often suffers from various kinds of degradations (e.g., noise, blur, rain drops, fog, color distortion, etc.), which severely affect vision-driven tasks (e.g., image classification, target recognition, and tracking, etc.). Thus, restoring the true scene from such degraded images is of significance. In recent years, a large body of deep learning-based image processing works has been exploited due to the advances in deep neural networks. This paper aims to make a comprehensive review of real-world image restoration algorithms and beyond. More specifically, this review provides overviews of critical benchmark datasets, image quality assessment methods, and four major categories of deep learning-based image restoration methods, i.e., based on convolutional neural network (CNN), generative adversarial network (GAN), Transformer, and multi-layer perceptron (MLP). The paper highlights the latest developments and advances in each category of network architecture to provide an up-to-date overview. Moreover, the representative state-of-the-art image restoration methods are compared visually and numerically. Finally, for real-world image restoration, the current situations are objectively assessed, challenges are discussed, and future directions and trends are presented.

With the development of Internet of Things (IoT) technology, embedded based electronic devices have penetrated every corner of our daily lives. As the brain of IoT devices, embedded based micro controller unit (MCU) plays an irreplaceable role. The functions of the MCUs are becoming more and more powerful and complicated, which brings huge challenges to embedded programmers. Embedded code, which is highly related to the hardware resources, differs from other popular programming code. The hardware configuration may be a big challenge to the programmers, who may only be good at software development and algorithm design. Online code searching can be time consuming and cannot guarantee an optimal approach. To solve this problem, in this paper, an embedded code classifier, which is designed to help embedded programmers to search for the most efficient code with precise tags, is demonstrated. A high quality embedded code dataset is built. A tag correlated multi-label machine learning model is developed for the embedded code dataset. The experimental results show that the proposed code dataset structure is proved to be more efficient on embedded code classification. The proposed embedded classifier algorithm shows a promising result on embedded code dataset. And it outperforms the traditional machine learning text classification models.

With the emergence of big data applications, stream join systems are widely used in extracting valuable information among multi-source streams. However, providing completeness of processing results in a large-scale distributed stream join system is challenging because it is hard to guarantee the consistency among all instances. We show through experiments that the abnormal result can make the quality of achieved data unacceptable in practice.In this paper, we propose Eunomia, a novel distributed stream join system which leverages an ordered propagation model for efficiently eliminating abnormal results. We design a light-weighted self-adaptive strategy to adjust the structure in the model according to the dynamic stream input rates and workloads. It can improve the scalability and performance significantly. We implement Eunomia and conduct comprehensive experiments to evaluate its performance. The results show that Eunomia eliminates abnormal results to guarantee the completeness, improves the system throughput by 25% and reduces the processing latency by 74% compared to state-of-the-art designs.