Abstract
The process of reinstating a clean background to an image that has been destroyed by multiple rain streaks and rain built up is called Image Deraining. We propose a single recurrent network first that begins by iteratively unfolding one shallow-residual network and then uses a recurrent layer to transfer the in-depth properties across stages. The traditional SRN (Single Recurrent Network) was used to learn both residual mapping and direct mapping for the removal of unwanted rain-streaks and anticipating a clean backdrop. With the combining of the SRNs into modified Bilateral Recurrent Network (BRN), the rain-streak layer and the backdrop can be exploited. Hence, we put forward a model using bilateral LSTMs (Long Short-Term Memory) that can transmit deep-features of rain-streak layer and backdrop layer between stages, as well as introduce the inter-play between SRNs, resulting in a BRN. The proposed modified_BRN performs better over the sophisticated methods on real-world and synthetic datasets, such as the popular datasets: Rain100H, Rain 100 L and Rain 12. The comparative analysis of the experimental results has been analysed on the two standard parameters: PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index Measure).
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The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
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Tejaswini, M., Sumanth, T.H. & Naik, K.J. Single image deraining using modified bilateral recurrent network (modified_BRN). Multimed Tools Appl 83, 3373–3396 (2024). https://doi.org/10.1007/s11042-023-15276-2
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DOI: https://doi.org/10.1007/s11042-023-15276-2