Deriving inherent optical properties from background color and underwater image enhancement

The contrast of remote sensing images captured in haze condition is poor, which influences their interpretation. In this letter, a novel dehazing algorithm based on the deformed haze imaging model is proposed. First, the model is deformed by introducing a translation term. Second, the atmospheric light and transmission are estimated according to the new model combined with dark channel prior. Lastly, the haze is successfully removed from remote sensing images using the proposed estimation algorithm. The estimated transmission is insensitive to the texture of ground objects, and the dehazing effect for nonuniform haze is more satisfactory than the compared method. Moreover, our approach can be used for general haze removal through adjusting the translation term. Experimental results reveal that the proposed method can recover the real scene clearly from haze remote sensing images along with the advantage of good color consistency.

Haze Removal for a Single Remote Sensing Image Based on Deformed Haze Imaging Model

Haze reduces the visibility of outdoor images. The majority of existing dehazing techniques perform poorly whenever objects are similar to airlight, and no shadow is cast on them. Also, existing approaches still suffer from the problems of colour distortion, edge preservation, and halo artefacts. To solve these issues, a modified gain coefficient filter based Dark channel prior (DCP) for single image dehazing is proposed. To reduce the colour distortion, restoration model of DCP is also redefined. The experimental results indicate that the proposed technique provides better results as compared with existing approaches.

Modified gain intervention filter based dehazing technique

To assist visually impaired people, a variety of obstacle detection (OD) methods with the monocular vision have been developed. Most conventional OD methods detect the obstacle by using the variation of the motion vector or the object size between two consecutive image frames. However, the OD performance using the short-term variation is significantly affected by the tracking error, which leads to inaccurate OD results. To cope with this problem, this paper presents a novel OD method based on a new structure, called Deformable Grid (DG). The DG is initially a regular gridshaped, but it can be deformed gradually depending on the motion of the object in the scene. The proposed method detects the object at risk of collision based on the degree of the deformation of the DG. Experimental results demonstrate that the proposed OD method outperforms the conventional ones in terms of processing time as well as accuracy.

A novel obstacle detection method based on deformable grid for the visually impaired

Haze degrades the quality of optical remote sensing data and reduces the accuracy of interpretation and classification. In this paper, we present an empirical single image-based haze removal method...

Single image dehazing for visible remote sensing based on tagged haze thickness maps

This study presents an algorithm for estimating three-dimensional (3D) depth from a single monocular outdoor image. This algorithm combines three major components: the initial depth prior, the dark channel prior, and multi-resolution analysis. The initial depth prior assigns an initial depth to each object based on its location in the image. The object segmentation in this step is based on edge, hue, and saturation. The dark channel prior cooperates with color saturation to generate approximate depth information. Combining the depth maps of both initial depth prior and dark channel prior generates depth maps of three resolutions. Fusing these maps together across different resolutions produces the final depth map. Experimental results show that the proposed method achieves a considerable computational savings compared to existing methods while retaining a depth map close to the ground truth.

Tien-Ying Kuo

Papers

Depth estimation from a monocular view of the outdoors