Abstract
To solve the problems of color distortion and structure blurring in images acquired by sensors during bad weather, an image dehazing algorithm based on feature learning is put forward to improve the quality of sensor images. First, we extracted the multiscale structure features of the haze images by sparse coding and the various haze-related color features simultaneously. Then, the generative adversarial network (GAN) was used for sample training to explore the mapping relationship between different features and the scene transmission. Finally, the final haze-free image was obtained according to the degradation model. Experimental results show that the method has obvious advantages in its detail recovery and color retention. In addition, it effectively improves the quality of sensor images.
Highlights
Visual information accounts for more than 80% of the total amount of information that is available to humans
Image dehazing aims to recover real scene information covered by fog in sensor images through relevant technical means and methods, to obtain complete details, structural information and natural, haze-free images
To better utilize features such as texture, structure, and color for improving the dehazing effect, we propose an image dehazing algorithm based on feature learning
Summary
Visual information accounts for more than 80% of the total amount of information that is available to humans. The image enhancement-based dehazing method improves the contrast of the restored image and highlights local features such as the details of the haze image. Many other improved methods have been proposed: Zhu [8] proposed the use of color attenuation and estimated the transmission through the linear model, which improved the operation efficiency of the method His method relied too much on image color information, resulting in an unsatisfactory effect when testing the entire haze image. Tang [10] adopted random forest to combine dark primary colors with multiple color features to improve the accuracy of transmission This method did not account for the textural features associated with haze, resulting in inaccurate estimation of the transmission.
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