Abstract:
Single-image de-raining is difficult because rain streaks vary in size, direction, and density. Rain streaks affect various parts of the image differently. Previous methods used prior information to remove rain streaks from a single image.
These methods ignore rain drop location information, which is a major drawback. We extend our previous work UMRL network and propose Image Quality-based single image Deraining using Confidence measure (QuDeC) to address this issue by learning the quality or distortion level of each patch in the rainy image and processing this information to learn the rain content at different scales.
We also guide the network to learn network weights based on the confidence measure about the estimate of quality at each location and residual rain streak information (residual map). The proposed method outperforms state-of-the-art methods in extensive synthetic and real dataset experiments.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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