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With the continuous development of TOF imaging technology, distance imaging technology has gradually been widely used as a part of three-dimensional spatial vision in many fields such as autonomous driving, mechanical obstacle avoidance, and virtual reality. However, due to the limited performance of current distance imaging sensors, the quality of depth images obtained from distance imaging is generally low, making it difficult to apply to computer vision tasks with high accuracy requirements. For the current problems of low accuracy, fewer boundary data, and poor dimensional resolution of TOF image, we introduce a TOF-CMOS image fusion approach found on DTCWT (Dual-Tree Complex Wavelet Transform). By utilizing the similarity between standard CMOS images and TOF images of the same scene in the edge area, the detailed information of the TOF images is enriched and their accuracy is improved. First, we applied the dual-tree complex wavelet transform applied to the source images. Second, we adopt a bilateral filter for the low frequency components of the TOF images to increase the accuracy of the images. Meanwhile, we extract the edge data in the high-frequency components of the CMOS images through the soft threshold method. Then, we blend the high-frequency components of the TOF images with the extracted high-frequency components of the CMOS images. In this paper, we innovatively propose fusion rules connecting the regional variance and the pixel maximum: we use the above two fusion methods respectively targeted according to the difference of data involved in the highest as well as the second-highest levels of high-frequency components. Finally, the images are reassembled by using the inverse dual-tree complex wavelet transform. We evaluate the merge results using evaluation indicators like average gradient, peak signal to-noise ratio, and so on. The laboratory results indicate that the method adequately enhances the visual effect of the TOF image and enriches the detailed information of the TOF image.
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