DehazeDM: Image Dehazing via Patch Autoencoder Based on Diffusion Models

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.