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Adaptive Modulation and Rectangular Convolutional Network for Stereo Image Super-Resolution
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DOI：10.1016/j.patrec.2022.07.018.png)
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Abstract
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Deep learning based stereo image super-resolution algorithm can make use of the additional information in the stereo image pairs to improve the quality of the reconstructed images. However, extracting the similarity information of one image to another precisely is challenging when partially occluded regions exist in stereo images. In this paper, we propose an adaptive modulation alignment mechanism to modulate the aligned features calculated from the parallax attention mechanism and effectively deal with the inaccuracy caused by occlusion. Furthermore, because disparities of stereo image exist only along the epipolar line, we use rectangular convolution kernel in some convolution layers, to extend the receptive field horizontally. Finally, experimental results demonstrate that our method achieves state-of-the-art performance on the Middlebury, KITTI 2012 and KITTI 2015 stereo benchmarks.(c) 2022 Elsevier B.V. All rights reserved.
Keywords:
stereo images
adaptive modulation
rectangular convolutional layer
super-resolution
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