Due to limitations of camera equipment, general cameras only capture a part for larger object scene. To extend the range of view and capture the whole scene, the camera may need to traverse the scene and scan multiple partially overlapping images and stitch them into one panoramic image of the scene. Recently, image mosaicing has been an active area of research in image processing and computer vision. More significant applications include photogrammetry measurement, virtual reality, video compression, video indexing, increasing the field of view and resolution of a camera. Chip microscope images mosaicing technology is important in the IC reverse design. Chip is a multilayer object. The number of chip microscope images is huge and the information is less or isomorphic in the overlapping region between two images, these influence the mosaicing quality seriously. Allowing for this, the algorithm based on the idea of topology graph can avoid the case that the local doesn’t align, a multilayer image mosaicing algorithm is proposed. The method first aligns images of same layers and images of different layers, and eliminates the effect of blank areas with 3D topology graph modification. Then it constructs the global image alignment model according to the modified 3D topology graph and the misalignment of multilayer image pairs. Last, it gets globally consistent estimation of multilayer image alignment parameters with nonlinear optimize method. Error accumulation is a key problem. It is due to loops of images sequence. Base on this, a new mosaicing algorithm based on loop consistent is proposed. The method first decomposes the topology into two parts, loops and non-loops. Then it separately optimizes the two parts with constrains about rotation and translation. This method can eliminate the effect of error accumulation and enhance the mosaicing quality. Mosaicing method based on multi-power images is proposed to avoid the case that the topology includes “isolated regions”. The method performs image mosaicing through estimating alignment parameters of high power images using corresponding low power images within the given alignment error range and then optimizing the mosaicing result. In the last, we design the whole mosaic system and give the method and result of preprocessing.
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