针对光照不足导致图像质量退化的问题, 提出了亮通道先验的Retinex算法用来补偿图像的光照强度。该算法假设局部恒常的光照可以初步满足光照均匀并与场景相似, 以亮通道运算对光照分量进行粗估计; 通常解决局部处理带来的分块效应问题是采用引导滤波方法, 但这会使补偿后的图像纹理模糊甚至丢失细节, 为此设计了基于图像结构相似性的融合策略。最后使用Retinex理论模型对光照进行补偿。实验结果表明: 所提算法简单高效, 能够对图像阴影或夜间图像的低照度区域进行快速地光照补偿, 在峰值信噪比(PNSR)上较传统算法提高了5 dB左右, 在结构相似性(SSIM)上比传统算法提高了7%以上。算法在纯软件系统的PC机上处理640×360的彩色视频时能达到6~12 ms/帧, 处理320×256的红外视频时达到4~10 ms/帧, 可满足工程需要。

Aiming at the problem of image quality degradation caused by insufficient illumination, a retractive algorithm of bright channel was proposed to compensate the illumination intensity of the image. The algorithm assumed that the local constant light could initially satisfy the uniformity of illumination and was similar to the scene, and the bright channel operation was used to estimate the weight of the light component. The problem of blocking was usually solved by the local processing, but this would make the compensation image texture blurred or even lost, and the fusion strategy based on image structure similarity was designed. Finally, the Retinex theoretical model was used to compensate for the light. The experimental results show that the proposed algorithm is simple and efficient, and can compensate for the low illumination area of image shadows or nighttime images. Compared with the traditional algorithm, the peak signal to noise ratio (PNSR) is improved by about 5 dB and the structure similarity (SSIM) increased by more than 7%. The algorithm in the pure software system PC (CPU frequency 2.4 G) processing 640×360 color video can reach 6-12 ms/frame, processing 320×256 infrared video to reach 4-10 ms/frame, to meet the needs of the project.