An Image Dehazing Method Based on Multi-Scale Densely Connected Networks
A connected network and multi-scale technology, which is applied in the field of image defogging based on multi-scale densely connected networks, can solve the problems of reduced performance of defogging, limited robustness, poor effect, etc., to improve contrast and improve defogging Effect, deep learning, simple and effective effect of defogging
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[0058] Such as Figure 1-8 As shown in one of them, the present invention discloses an image defogging method based on a multi-scale dense connection network, which is divided into the following steps:
[0059] Step 1, training data preparation phase.
[0060] Described step 1 specifically comprises the following steps:
[0061] Step 1.1, select the training data set. What the present invention uses is the match data of CVPR NTIRE2018 Outdoor Dehaze, and it contains fog image and non-fog image pair. Wherein, the foggy image is formed by a certain algorithm from the fogless image.
[0062] In step 1.2, the image database is preprocessed to form a paired set of haze sub-images and high-definition haze-free sub-images. Adaptive histogram equalization method based on contrast limitation [9] Preprocess the foggy image I(x) to obtain the contrast-adjusted foggy image I(x 1 ). From the foggy image I(x 1 ) in the screenshot sub-image I by d*d (d=256 in the present invention) ...
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