Model Assisted Multi-band Fusion for Single Image Enhancement and Applications to Robot Vision

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This paper presents a fast single image enhancement that is applicable regardless of channels in various environments. The main idea of the paper is combining model-based and fusion-based dehazing methods, thereby presenting balanced image enhancement while elaborating image details. The proposed method enhances both color and grayscale images without any prior information. Multiband decomposition is utilized to extract the base and detail layers for intensity and Laplacian modules. The proposed ambient map and transmission estimation for the intensity module are effective in restoring the true intensity. Adaptive nonlinear mapping functions adjust details on each residual layer. Through color-corrected reconstruction, our results demonstrate outstanding performance on various types of hazy images. The proposed method is thoroughly validated in terms of conventional image quality comparison. We also provide the evaluation at the application phase from both the semantic (segmentation) and geometric (direct odometry) vision based robotics application. The overall algorithm is presented in https://youtu.be/3Fk3kbaPkXQ.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2018-10
Language
English
Citation

IEEE ROBOTICS AND AUTOMATION LETTERS, v.3, no.4

ISSN
2377-3766
DOI
10.1109/LRA.2018.2843127
URI
http://hdl.handle.net/10203/244524
Appears in Collection
CE-Journal Papers(저널논문)
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