Yu Wenyong, Yao Haiming, Li Dan, Li Gangyan, Shi Hui
School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430074, China.
Sensors (Basel). 2021 Jan 27;21(3):845. doi: 10.3390/s21030845.
Low-contrast or uneven illumination in real-world images will cause a loss of details and increase the difficulty of pattern recognition. An automatic image illumination perception and adaptive correction algorithm, termed as GLAGC, is proposed in this paper. Based on Retinex theory, the illumination of an image is extracted through the discrete wavelet transform. Two features that characterize the image illuminance are creatively designed. The first feature is the spatial luminance distribution feature, which is applied to the adaptive gamma correction of local uneven lighting. The other feature is the global statistical luminance feature. Through a training set containing images with various illuminance conditions, the relationship between the image exposure level and the feature is estimated under the maximum entropy criterion. It is used to perform adaptive gamma correction on global low illumination. Moreover, smoothness preservation is performed in the high-frequency subband to preserve edge smoothness. To eliminate low-illumination noise after wavelet reconstruction, the adaptive stabilization factor is derived. Experimental results demonstrate the effectiveness of the proposed algorithm. By comparison, the proposed method yields comparable or better results than the state-of-art methods in terms of efficiency and quality.
现实世界图像中的低对比度或光照不均匀会导致细节丢失，并增加模式识别的难度。本文提出了一种名为GLAGC的自动图像光照感知与自适应校正算法。基于视网膜理论，通过离散小波变换提取图像的光照。创造性地设计了两个表征图像照度的特征。第一个特征是空间亮度分布特征，用于局部光照不均匀的自适应伽马校正。另一个特征是全局统计亮度特征。通过一个包含各种光照条件图像的训练集，在最大熵准则下估计图像曝光水平与该特征之间的关系。它用于对全局低光照进行自适应伽马校正。此外，在高频子带中进行平滑度保留以保持边缘平滑度。为了消除小波重构后的低光照噪声，推导了自适应稳定因子。实验结果证明了该算法的有效性。相比之下，在效率和质量方面，该方法比现有方法产生了相当或更好的结果。
