2019
DOI: 10.1111/cgf.13825
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Rain Wiper: An Incremental Randomly Wired Network for Single Image Deraining

Abstract: Single image rain removal is a challenging ill‐posed problem due to various shapes and densities of rain streaks. We present a novel incremental randomly wired network (IRWN) for single image deraining. Different from previous methods, most structures of modules in IRWN are generated by a stochastic network generator based on the random graph theory, which ease the burden of manual design and further help to characterize more complex rain streaks. To decrease network parameters and extract more details efficie… Show more

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Cited by 7 publications
(3 citation statements)
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“…Existing methods can be divided into two classes depending on the order of the restoration and detection tasks. The most common methods are to preprocess low quality images with existing restoration algorithms to remove haze [28][6][7] [5] or rain [29][30] [31]. The preprocessed images are then fed into object detection networks to generate detection results.…”
Section: B Object Detection In Adverse Weather Conditionsmentioning
confidence: 99%
“…Existing methods can be divided into two classes depending on the order of the restoration and detection tasks. The most common methods are to preprocess low quality images with existing restoration algorithms to remove haze [28][6][7] [5] or rain [29][30] [31]. The preprocessed images are then fed into object detection networks to generate detection results.…”
Section: B Object Detection In Adverse Weather Conditionsmentioning
confidence: 99%
“…Thereafter, many efforts have been made to either introduce advanced network modules and structures, or integrate problem‐related knowledge into network design. Network modules, such as dense block [FWF*18], recursive block [RZH*19] and dilated convolution [LWL*18,YTF*17,LQS*19,RLHS20b,RLHS20a,DWW*20], and structures, such as RNN [LWL*18, RZH*19], GAN [LCT19, ZSP19] and multi‐stream networks [YTF*17, LWL*18], are validated to be effective in rain streaks removal. Auxiliary information, including rain density [ZP18b], streak position [YTF*17] and gradient information [WZL*19], are leveraged to improve the robustness and performance of rain removal networks.…”
Section: Related Workmentioning
confidence: 99%
“…Compared with general object detection, few research efforts have been explored on object detection in adverse weather conditions. Early methods mainly focused on pre‐processing the degraded images by existing restoration algorithms such as image dehazing [HST11, QWB*20, WYG*22, SZB*22] or image deraining [LQS*19, RLHS20a, DWW*20], and then sending the processed images to the subsequent detection network for object detection. Although employing image restoration approaches as a preprocessing step can improve the overall quality of degraded images, these images may not definitely benefit the detection performance.…”
Section: Related Workmentioning
confidence: 99%