|
Deep convolutional networks have become a popular tool for image generation and restoration.In general, their outstanding performance is attributed to their ability to learn real image priors from a large number of example images.Many seemingly unrelated computer vision tasks can be viewed as special cases of image decomposition into independent layers.Image segmentation (divided into foreground layer and background layer); transparent layer separation (divided into reflection layer and transmission layer); image dehazing (separation into clear image and haze map), etc.Nowadays, in the field of image restoration using deep learning, there are many low-quality images, but the corresponding original pictures are fewer, which makes image restoration based on deep learning one of the more difficult problems in the field of computer vision.In order to break through the related problems, this paper abandons the supervised deep learning method based on a large amount of data training and then proposes an autonomous unsupervised image inpainting network, Fast-RDDIP, based on structure prior.This ability stems from the fact that the internal statistics of a mixture of layers are more complex than the statistics of each individual component.Consequently, this study constructs a matching network structure using the characteristics of various learning distribution rates of the network and additional convolution, which has characteristics like shared parameters. can perform end-to-end training.This essay explains how this technique improves image quality and dehazing. According to the experimental findings on the O-Haze dataset, Fast-RDDIP performs better than more established methods like SIHR, EIDBC, supervised neural networks like DehazeNet, NTDF, NLD, URIE, and BOPBL, as well as unsupervised neural networks like DoubleDIP models.
|
