Images taken under low light conditions tend to suffer from poor visibility, which can decrease image quality and even reduce the performance of downstream tasks. It is hard for a CNN-based method to learn generalized features that can recover normal images from the ones under various unknown low light conditions. We propose to incorporate the contrastive learning into an illumination correction network to learn abstract representations to distinguish various low light conditions in the representation space, with the purpose of enhancing the generalizability of the network. Considering that light conditions can change the frequency components of the images, the representations are learned and compared in both spatial and frequency domains to make full advantage of the contrastive learning. Additionally, a grayscale self-weight perception method is used to preproccess the images to reduce the complexity of the model in coping with the uneven distribution of image illumination. The proposed method is evaluated on LOL and LOL-V2 datasets, and the results show that the proposed method achieves better qualitative and quantitative results compared with other state-of-the-art methods.
New phase change materials development has become one of the most critical modules in the fabrication of low power consumption and good data retention phase change memory (PCM). Among various candidates of new phase change materials, SiSbTe (SST) is one of the most promising materials due to its benefits of low RESET current, high crystallization temperature, good adhesion and small volume shrinkage during phase change from amorphous to crystalline state. However, the oxidization of SST film was found when exposing to the atmosphere. By analyzing the depth profile of chemical states, we found oxygen more easily penetrated into the SST film and bonded with Si and Sb compared to GeSbTe (GST) film. The oxidization mechanism between SST and GST was briefly discussed. We achieved 80% improvement of oxidization issue by nitrogen and argon surface treatment. We proposed a manufacturing solution of SST for PCM.
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