基于子带加权融合的多尺度Retinex图像增强算法

Retinex模型是人类视觉对亮度和色彩的感知模型，多尺度Retinex算法具有动态范围压缩大、色彩恒常性、色彩保真度高等特点，被广泛运用于低光照图像的增强。提出一种新的图像增强算法，首先用混合灰度变换函数替代传统的灰度变换函数，对图像做不同尺度的Retinex变换，并分解重复的频带信息，对各个独立的频带分别进行增强。然后引入图像融合的思想，将不同尺度Retinex图像的每个像素点都赋予不同的权重。最后得到输出图像。实验结果证明，本算法可有效增强图像的对比度，得到的图像清晰度高，色彩恢复好。

The multi-scale retinex algorithm for image enhancement based on sub-band weighting fusion

The Retinex model refers to human perception of lightness and color. The multi-scale Retinex（MSR） algo- rithm, which is high dynamic range, color constancy and high color fidelity, is widely used in low-light image enhance- ment. A novel multi-scale retinex method based on sub-band weighting fusion for image enhancement was proposed. First, hybrid intensity transfer function was used to have retinex outputs in different scales. Then the retinex outputs were decomposed into non-overlapping spectral sub-bands. Image enhancement was processed in each sub-band. Final, the resulted image was fused by weighting every point of each Retinex outputs. The experimental results show that the proposed algorithm effectively enhances the contrast of original image and achieve a visual-pleasing and color-vivid outputs.