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

TLDR

The overall algorithm is presented in a publicly available video. The study proposes a fast single‑image enhancement that combines model‑based and fusion‑based dehazing to provide balanced detail preservation across channels and environments. The method employs multiband decomposition to extract base and detail layers for intensity and Laplacian modules, uses an ambient map and transmission estimation for intensity restoration, applies adaptive nonlinear mapping to residual layers, and is validated through conventional quality metrics and robotics applications such as semantic segmentation and direct odometry. Color‑corrected reconstruction shows the approach achieves outstanding performance on a variety of hazy images.

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.