Adaptive compressive coding method based on spectral image region segmentation

In the field of compressive sensing spectral imaging, an adaptive coding method based on a-prior knowledge is a way to obtain high-precision scene information. In this paper, we propose a method that uses low-resolution spatial-spectral information to split into homogeneous regions before generating adaptive coding matrices, in response to the shortcomings of most existing adaptive coding methods that use only spatial a priori information to generate coding matrices. The method uses coding devices in a compressive spectral imaging system to obtain spectral a-priori information with low spatial resolution. Based on this a-priori information, an adaptive segmentation method with region merging is used to obtain segmented images with certain regional homogeneity. The adaptive coding theory and this segmentation result are combined to generate the adaptive coding matrix, and then the compressive observation information of the scene and its complementary observation information are obtained. Based on these observations, the scene information with a high spatial resolution is calculated by the reconstruction algorithm. Simulation experiments show that the adaptive compressive coding method based on spectral image region segmentation has advantages in peak signal-to-noise ratio and structural consistency rating indexes compared with traditional adaptive coding methods.