Shadow in an image is important visual information of the projective object, but it affects computer vision tasks. Existing single image shadow removal methods cannot obtain good shadow-free results due to the lack of robust shadow features or insufficiency of and errors in training sample data. In order to generate accurately the shadow mask image for describing the illumination attenuation degree and obtain the high quality shadow-free image, a single image shadow removal method based on attenuated generative adversarial network was proposed. Firstly, an attenuator guided by the sensitive parameters was used to augment the training sample data in order to provide shadow sample images agreed with physical illumination model for a subsequent generator and discriminator. Then, with the supervision from the discriminator, the generator combined perceptual loss function to generate the final shadow mask. Compared with related works, the proposed method can effectively recover the illumination information of shadow regions and obtain the more realistic shadow-free image with natural transition of shadow boundary. Shadow removal results were evaluated using objective metric. Experimental results show that the proposed method can remove shadow effectively in various real scenes with a good visual consistency.
To resolve the problem that the existing adaptive slicing algorithm in 3D printing cannot retain effectively model characteristics, a new adaptive slicing method for recognizing and retaining model characteristics was proposed. Firstly, the definition of model characteristic was extended, and the concept of loss and offset of model characteristic was introduced. Secondly, a characteristic recognition method was proposed, the key point of which is to make use of the fact that the surface complexity and number of contours must change around the model characteristics. Finally, based on existing adaptive slicing algorithms, this algorithm retained model characteristics by slicing the model with minimum layer thickness near the model characteristics. On the self-developed software Slicer3DP, the following algorithms were implemented: the uniform slicing algorithm, the adaptive slicing algorithm and the proposed slicing algorithm. By comparing these algorithms, it is found that the proposed slicing algorithm resolves effectively the loss and offset of model characteristics while maintaining both slicing precision and efficiency. The result shows that the proposed method can be used for 3D printing with high precision requirement.