JOURNAL ARTICLE

A fusion-based enhancing approach for single sandstorm image

Abstract

In this paper, a novel image enhancing approach focuses on single sandstorm image is proposed. The degraded image has some problems, such as color distortion, low-visibility, fuzz and non-uniform luminance, due to the light is absorbed and scattered by particles in sandstorm. The proposed approach based on fusion principles aims to overcome the aforementioned limitations. First, the degraded image is color corrected by adopting a statistical strategy. Then two inputs, which represent different brightness, are derived only from the color corrected image by applying Gamma correction. Three weighted maps (sharpness, chromaticity and prominence), which contain important features to increase the quality of the degraded image, are computed from the derived inputs. Finally, the enhanced image is obtained by fusing the inputs with the weight maps. The proposed method is the first to adopt a fusion-based method for enhancing single sandstorm image. Experimental results show that enhanced results can be improved by color correction, well enhanced details and local contrast while promoted global brightness, increasing the visibility, naturalness preservation. Moreover, the proposed algorithm is mostly calculated by per-pixel operation, which is appropriate for real-time applications.

Keywords:
Artificial intelligence Visibility Brightness Computer vision Computer science Luminance Pixel Image fusion Naturalness Image (mathematics) Color image Distortion (music) Image quality Fusion Image processing Optics Physics

Metrics

69
Cited By
0.48
FWCI (Field Weighted Citation Impact)
25
Refs
0.69
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Image Enhancement Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Image Fusion Techniques
Physical Sciences →  Engineering →  Media Technology
Advanced Image Processing Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
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