Uncertainty Guided Multi-Scale Residual Learning-Using a Cycle Spinning CNN for Single Image De-Raining

By Rajeev Yasarla, Vishal M. Patel. Computer Vision and Pattern Recognition, 2019

338 citations recorded by the source corpus.

Single image de-raining is an extremely challenging problem since the rainy image may contain rain streaks which may vary in size, direction and density. Previous approaches have attempted to address this problem by leveraging some prior information to remove rain streaks from a single image. One of the major limitations of these approaches is that they do not consider the location information of rain drops in the image. The proposed Uncertainty guided Multi-scale Residual Learning (UMRL) network attempts to address this issue by learning the rain content at different scales and using them to estimate the final de-rained output. In addition, we introduce a technique which guides the network to learn the network weights based on the confidence measure about the estimate. Furthermore, we introduce a new training and testing procedure based on the notion of cycle spinning to improve the final de-raining performance. Extensive experiments on synthetic and real datasets to demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art methods.

Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/45b2f6368a570c3a4e0349050541c23ae243eb41

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In Computer Graphics and Computer-Aided Design

In Computer Vision and Pattern Recognition

Citations in the corpus

JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method (2020, methodology): “Inspired by uncertainty estimation in CNNs [39, 40, 41, 74], we aim to model pixel-wise aleatoric uncertainty of the residuals estimated by CBi.”
TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions (2021, background): “Recently, Convolutional Neural Networks (CNNs) based solutions have been explored extensively for deraining [11, 36, 52, 55, 60, 62, 68, 69, 75], dehazing [8, 19, 40, 56, 68, 70, 71], desnowing [29, 43, 72] and raindrop removal [36, 39, 65].”
All-In-One Image Restoration for Unknown Corruption (2022, background): “To be specific, the denoising baselines contain CBM3D [5], DnCNN [53], IRCNN [54], FFDNet [55] and BRDNet [40]. the deraining baselines are DIDMDN [52], UMRL [50], SIRR [46], MSPFN [17], and LPNet [11].”
Multi-Stage Progressive Image Restoration (2021, methodology): “Rainy Image UMRL [82] RESCAN [47] PreNet [63] MSPFN [37] MPRNet (Ours)”
Deep Generalized Unfolding Networks for Image Restoration (2022, methodology): “We compare our proposed DGUNet with eight competitive methods [19,31,38,54,68,72,79,81].”
Multi-Scale Progressive Fusion Network for Single Image Deraining (2020, methodology): “These representative methods include DerainNet [6], RESCAN [19], DIDMDN [40], UMRL [37], SEMI [31] and PreNet [26].”
Restormer: Efficient Transformer for High-Resolution Image Restoration (2021, background): “Reference Rainy DerainNet [21] SEMI [81] UMRL [87]”