Image deraining is a low-level restoration task that has become quite popular during the past decades. Although recent data-driven deraining models exhibit promising results, most of these models are trained on synthetic rain data sets which do not generalize well when applied to real rain images. While recent real-rain data sets have achieved favorable generalization performance, generating rain-free ground-truths can be tedious and time-consuming. To address this problem, in this work, we present rain to rain training, an unsupervised training method for single image deraining. Our experiments show that it is possible to train single image deraining models by using only rain images. This can be achieved by simply training models to map pairs of rain images. We also introduce the idea of using the least overlapping training pairs, a method of selecting adequate training pairs that enables rain to rain training to achieve equivalent deraining performance compared to supervised training.INDEX TERMS image restoration, real rain, synthetic rain, single image deraining, unsupervised training.
With the development of deep learning, researches in the field of computer vision are attracting more attention. As the pre-processing operation of visual tasks, a salient model may focus on pure architectures. The paper proposes a new multi-scale fusion network to enrich high-level redundant information with the enlarged receptive field. With the guidance of attention mechanism, the framework can capture more effective correlation spatial and channels information. Building a short-connection between high-level and each level features to transmit the contextual features. The model can be used in a variety of complex scenes for end-to- end image detection, with simple structure and strong versatility. Experimental results obtained on multiple common datasets have shown that the proposed model achieved better performance both in the visual effect and the accuracy for small object and multi-target detection.
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