SANet: An End-to-End Anaglyph Image Separation Network

Tao Yan, Lu Tan, Yiwei Lu, Cihang Wei, Jinsheng Liu, Rynson W. H. Lau

Abstract: Anaglyph image separation recovering a stereo image pair from an anaglyph image is a classical and ill-posed image recovery problem. We observed that there is always an interpenetration between the contents of the left and right views of an anaglyph image, as image compression and decompression are always adopted during the transmission of anaglyph images via Internet. Existing anaglyph image separation methods always produce stereo image pairs with obvious color degradation or structural distortion. In this article, we propose an end-to-end network, called Stereo Image Separation from Anaglyph Images (SANet), consisting of two main parts: AIS-Net and DE-Net. The AIS-Net employs residual refinement module to separate the initial features extracted by VGG for reconstructing the left and right views. To address interpenetration between left and right views, we propose BiFIB, which separates residual information from one view and transfers it to another view. Since depth estimation from the recovered left and right views relies on the recovery of structural features for the left and right views, DE-Net is assumed to provide useful constraint to our SANet. Extensive experiments conducted on several representative datasets demonstrate the effectiveness and superiority of our method compared with existing methods. Our code and models are available at: https://github.com/YT3DVision/SANet.

External IDs:dblp:journals/tce/YanTLWLL25