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

Younggun Cho; Jinyong Jeong; Ayoung Kim

RA-L 201886 citations

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

Abstract

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.

BibTeX

@inproceedings{ral2018_modelassistedmul,
  title = {Model-Assisted Multiband Fusion for Single Image Enhancement and Applications to Robot Vision},
  author = {Younggun Cho and Jinyong Jeong and Ayoung Kim},
  booktitle = {RA-L 2018},
  year = {2018}
}