计算机科学
自编码
人工智能
图像(数学)
计算机视觉
编码器
高保真
帧(网络)
感知
任务(项目管理)
忠诚
扩散
深度学习
模式识别(心理学)
物理
热力学
神经科学
电气工程
生物
工程类
操作系统
电信
管理
经济
作者
Yuming Yang,Dongsheng Zou,Xinyi Song,Xiaotong Zhang
标识
DOI:10.1109/smc53992.2023.10394653
摘要
Image dehazing is a crucial computer vision application with the primary objective of estimating haze-free images from hazy images. Deep neural network architectures have emerged as the dominant approaches and achieved remarkable progress. However, due to the intricacy, existing dehazing methods need help to train large deep learning networks. This work proposes a novel image dehazing network based on Diffusion Model (DehazeDM). Firstly, by segmenting the image into patches during the sampling procedure, we can dehaze images of arbitrary size. Then we compress the image into the latent space via the auto-encoder model and conduct the diffusion operation in the latent space, significantly decreasing the computational complexity associated with the task while exhibiting negligible effects on the perceptual fidelity of the resultant images. Extensive experiments verify the effectiveness and the superior performance of DehazeDM in image dehazing.
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