Rain Removal from a Single Image Using Refined Inception ResNet v2
Circuits, Systems, and Signal Processing, ISSN: 1531-5878, Vol: 42, Issue: 6, Page: 3485-3508
2023
- 13Citations
- 5Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
The atmospheric conditions like rain degrade visibility, creating problems for computer vision applications. In single-image de-raining, the lack of temporal information creates challenges. Rain removal requires a high-frequency layer extraction, as rain affects an image’s high-frequency layer (detail layer). This article has proposed using a combination of image decomposition and Inception ResNet v2 (IR v2) network for single-image rain removal. A guided filter decomposes the rainy image into the base and detail layers. The use of the Inception ResNet v2 (IR v2) network is proposed to create the residual map of the rain streaks from the high-frequency layer of the input image. The de-rained image obtained by subtracting the residual from the original rainy image provides comparable results. SSIM is added to MSE to train the IR v2 network, which improves the network’s performance. The complexity of the network (IR v2) is high. A systematic reduction in the network is done first at the module level and later at convolution filters. The proposed refinement decreases network complexity and reduces execution time without degrading the performance. An extensive test using natural and artificial rainy images reveals that the proposed refined Inception ResNet v2 (rIR v2) favorably competes against the recent single-image de-raining techniques.
Bibliographic Details
Springer Science and Business Media LLC
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