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Missing Recovery: Single Image Reflection Removal Based on Auxiliary Prior Learning | IEEE Journals & Magazine | IEEE Xplore

Missing Recovery: Single Image Reflection Removal Based on Auxiliary Prior Learning


Abstract:

Photographs taken through a glass window are susceptible to disturbances due to reflection. Therefore, single image reflection removal is crucial to image quality enhance...Show More

Abstract:

Photographs taken through a glass window are susceptible to disturbances due to reflection. Therefore, single image reflection removal is crucial to image quality enhancement. In this paper, a novel learning architecture that can address this ill-posed problem is proposed. First, a novel reflection removal pipeline was designed to reconstruct the missing information caused by the camera imaging process using the proposed missing recovery network. Second, to address the issues in existing reflection removal strategies, we revisit several auxiliary priors and integrate them by defining an energy function. To solve the energy function, a convolutional neural network-based optimization scheme was proposed. Finally, we investigated the dark channel responses of reflection and clean images and found an interesting way to distinguish between these two types of images. We prove this property mathematically and propose a novel loss function called dark channel loss to improve performance. Experiments show that the proposed method outperforms state-of-the-art reflection removal methods both quantitatively and qualitatively.
Published in: IEEE Transactions on Image Processing ( Volume: 32)
Page(s): 643 - 656
Date of Publication: 28 December 2022

ISSN Information:

PubMed ID: 37015621

Funding Agency:


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