A Model of Retinal Local Adaptation for the Tone Mapping of Color Filter Array Images

We present a tone mapping algorithm that is derived from a model of retinal processing. Our approach has two major improvements over existing methods. First, tone mapping is applied directly on the mosaic image captured by the sensor, analogue to the human visual system that applies a non-linearity on the color signals captured by the cone mosaic. This reduces the number of necessary operations by a factor three. Second, we introduce a variation of the center/surround class of local tone mapping algorithms, which are known to increase the local contrast of images but tend to create artifacts. Our method gives a good improvement in contrast while avoiding halos and maintaining good global appearance. Like traditional center/surround algorithms, our method uses a weighted average of surrounding pixel values. Instead of using it directly, the weighted average result serves as a variable in the Naka-Rushton equation, which models the photoreceptor non-linearity. Our algorithm provides pleasing results on various images with different scene content, key, and dynamic range.