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CVPR 2021 Open Access Repository
Source: openaccess.thecvf.com
Article summary:
1. A novel contrastive regularization (CR) is proposed to exploit both the information of hazy images and clear images as negative and positive samples, respectively.
2. A compact dehazing network based on autoencoder-like (AE) framework is developed to balance performance and memory storage.
3. Extensive experiments demonstrate that the proposed AECR-Net surpasses the state-of-the-art approaches.
Article analysis:
The article provides a detailed description of a novel contrastive regularization (CR) for single image dehazing, as well as a compact dehazing network based on autoencoder-like (AE) framework. The authors provide evidence from extensive experiments that their proposed AECR-Net surpasses the state-of-the-art approaches in terms of performance and memory storage.
The article appears to be reliable and trustworthy, with no obvious biases or unsupported claims. All claims are supported by evidence from experiments, and all potential risks are noted. The article does not appear to be promotional in nature, nor does it present only one side of an argument without exploring counterarguments or missing points of consideration.