Abstract
Building a mathematical model of uneven illumination and contrast is difficult, even impossible. This paper presents a novel image balancing method for a satellite image. The method adjusts the mean and standard deviation of a neighborhood at each pixel and consists of three steps, namely, elimination of coarse light background, image balancing, and max-mean-min radiation correction. First, the light background is roughly eliminated in the frequency domain. Then, two balancing factors and linear transformation are used to adaptively adjust the local mean and standard deviation of each pixel. The balanced image is obtained by using a color preserving factor after max-mean-min radiation correction. Experimental results from visual and objective aspects based on images with varying unevenness of illumination and contrast indicate that the proposed method can eliminate uneven illumination and contrast more effectively than traditional image enhancement methods, and provide high quality images with better visual performance. In addition, the proposed method not only restores color information, but also retains image details.
Highlights
Satellite images are prone to the phenomenon of uneven illumination and contrast because of the atmospheric environment and climate condition while these images are being acquired
The proposed method consists of three steps: elimination of coarse light background, image balancing, and max-mean-min radiation correction
Uneven illumination and contrast still exists in images processed by using Homographic filter (HF), minimum mean brightness error bi-histogram equalization (MMBEBHE), recursive mean separate histogram equalization (RMSHE), and MSR with color restoration (MSRCR); in contrast, the image results of the proposed method show high quality and good visual performance without any uneven illumination and contrast
Summary
Satellite images are prone to the phenomenon of uneven illumination and contrast because of the atmospheric environment and climate condition while these images are being acquired. Satellite images that cover large areas, especially mosaic images, exhibit both uneven illumination and contrast distribution in some local areas [1,2] This phenomenon has a negative influence on the further analysis and application of images since it will seriously affect the image quality and visual experience for human beings. Many histogram-based approaches [3,4,5], such as histogram equalization, brightness preserving bi-histogram equalization, minimum mean brightness error bi-histogram equalization (MMBEBHE), recursive mean separate histogram equalization (RMSHE), and light balancing [6,7] have been used to adjust the illumination and contrast of an image and these methods successfully enhance illumination and contrast while preserving input brightness to some extent. The proposed method consists of three steps: elimination of coarse light background, image balancing, and max-mean-min radiation correction
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