Contrast Enhancement of Brightness-Distorted Images by Improved Adaptive Gamma Correction
This work addresses a specific issue in image processing for real-world scenarios like improper exposure, but it is incremental as it builds upon existing adaptive gamma correction methods.
The paper tackled the problem of contrast enhancement for brightness-distorted images, including globally bright images and dimmed images with local bright regions, by proposing an improved adaptive gamma correction algorithm that uses negative images and truncated CDF modulation, resulting in consistently good enhancement results as shown in qualitative and quantitative experiments.
As an efficient image contrast enhancement (CE) tool, adaptive gamma correction (AGC) was previously proposed by relating gamma parameter with cumulative distribution function (CDF) of the pixel gray levels within an image. ACG deals well with most dimmed images, but fails for globally bright images and the dimmed images with local bright regions. Such two categories of brightness-distorted images are universal in real scenarios, such as improper exposure and white object regions. In order to attenuate such deficiencies, here we propose an improved AGC algorithm. The novel strategy of negative images is used to realize CE of the bright images, and the gamma correction modulated by truncated CDF is employed to enhance the dimmed ones. As such, local over-enhancement and structure distortion can be alleviated. Both qualitative and quantitative experimental results show that our proposed method yields consistently good CE results.