Multi-Scale Adaptive Weighted Network for Polarization Computational Imaging Super-Resolution

Due to the pixel limit of the polarization imaging detector and the object detection conditions, the spatial resolution of the object polarization image in actual imaging detection applications is generally low. Convolutional neural networks (CNNs) have been introduced to image super-resolution (SR). However, these methods rarely explore the internal connections between local spatial components and multi-scale feature maps in different receptive fields. To this end, we propose a multi-scale adaptive weighted network (MSAWN) for polarization computational imaging super-resolution to gain superior reconstruction performance. Computational imaging methods centered on information acquisition and interpretation can obtain high-resolution images that are superior to imaging systems. Specifically, we use a limited amount of memory and computational power even with multi-scale information and multi-level polarization. Secondly, a spatial pyramid structure based on space-channel attention mechanism is designed to effectively adjust the feature weight of polarization information. Thirdly, we adopt an adaptive weight unit to reduce redundant network branches and parameters. Particularly, we introduce an innovative reconstruction layer with inputs coming from multiple paths by mean of sub-pixel convolution. The experimental results show that the proposed method achieves better reconstruction accuracy and visual effect, and the objective evaluation indexes such as PSNR and SSIM are significantly improved.