SFA-Net: A Selective Features Absorption Network for Object Detection in Rainy Weather Conditions.

In recent years, object detection approaches using deep convolutional neural networks (CNNs) have derived major advances in normal images. However, such success is hardly achieved with rainy images due to lack of visibility. Aiming to bridge this gap, in this article, we present a novel selective features absorption network (SFA-Net) to improve the performance of object detection not only in rainy weather conditions but also in favorable weather conditions. SFA-Net accomplishes this objective by utilizing three subnetworks, where the feature selection subnetwork is concatenated with the object detection subnetwork through the feature absorption subnetwork to form a unified model. To promote further advancement in object detection impaired by rain, we propose a large-scale rainy image dataset, named srRain, which contains both synthetic rainy images and real-world rainy images for training and testing purposes. srRain is comprised of 25 900 rainy images depicting diverse driving scenarios in the presence of rain with a total of 181 164 instances interpreting five common object categories. Experimental results display that our SFA-Net reaches the highest mean average precision (mAP) of 77.53% on a normal image set, 62.52% on a synthetic rainy image set, 37.34% on a collected natural rainy image set, and 32.86% on a published real rainy image set, surpassing current state-of-the-art object detectors and the combination of image deraining and object detection models while retaining a high speed.

An Advanced Noise Reduction and Edge Enhancement Algorithm.

Complementary metal-oxide-semiconductor (CMOS) image sensors can cause noise in images collected or transmitted in unfavorable environments, especially low-illumination scenarios. Numerous approaches have been developed to solve the problem of image noise removal. However, producing natural and high-quality denoised images remains a crucial challenge. To meet this challenge, we introduce a novel approach for image denoising with the following three main contributions. First, we devise a deep image prior-based module that can produce a noise-reduced image as well as a contrast-enhanced denoised one from a noisy input image. Second, the produced images are passed through a proposed image fusion (IF) module based on Laplacian pyramid decomposition to combine them and prevent noise amplification and color shift. Finally, we introduce a progressive refinement (PR) module, which adopts the summed-area tables to take advantage of spatially correlated information for edge and image quality enhancement. Qualitative and quantitative evaluations demonstrate the efficiency, superiority, and robustness of our proposed method.

The role of cation and anion dopant incorporated into a ZnO electron transporting layer for polymer bulk heterojunction solar cells.

Doping is a widely-implemented strategy for enhancing the inherent electronic properties of charge transport layers in photovoltaic devices. A facile solution-processed zinc oxide (ZnO) and various cation and anion-doped ZnO layers were synthesized via the sol-gel method and employed as electron transport layers (ETLs) for inverted polymer solar cells (PSCs). The results indicated that all PSCs with doped ZnO ETLs exhibited better photovoltaic performance compared with the PSCs with a pristine ZnO ETL. By exploring the role of various anion and cation dopants (three compounds with the same Al3+ cation: Al(acac)3, Al(NO3)3, AlCl3 and three compounds with the same Cl- anion: NH4Cl, MgCl2, AlCl3), we found that the work function changed to favor electronic extraction only when the Cl anion was involved. In addition, the conductivity of ZnO was enhanced more with the Al3+ cation. Therefore, in inverted solar cells, doping with Al3+ and Cl- delivered the best power conversion efficiency (PCE). The maximum PCE of 10.38% was achieved from the device with ZnO doped with Al+ and Cl-.