#INPAINT CRACK GENERATOR#
#INPAINT CRACK MANUAL#
#INPAINT CRACK CRACK#

The proposed approach is evaluated against the current state-of-the-art image inpainting methods. In the color correction, CCN operates a local color adjustment for missing pixels which reduces the negative effects of color bias and edge jumping. In the structure reconstruction, line drawings are used by SRN as a guarantee for large-scale content authenticity and structural stability. It divides the inpainting process into two steps: structure reconstruction and color correction, executed by a structure reconstruction network (SRN) and a color correction network (CCN), respectively. To this end, in this paper, we propose a line drawing guided progressive mural inpainting method. Moreover, due to the discrete colors in paints, mural inpainting may suffer from apparent color bias as compared to natural image inpainting. These methods obtain high performance in restoration or completion of some specific objects, e.g., human face, fabric texture, and printed texts, etc., however, are not suitable for repairing murals with varied subjects, especially for murals with large damaged areas. Most existing image-inpainting methods tend to take a target image as the only input and directly repair the damage to generate a visually plausible result. Mural image inpainting refers to repairing the damage or missing areas in a mural image to restore the visual appearance.

#INPAINT CRACK MANUAL#

It can be used as a reference for experts in manual inpainting, saving the cost and time of manual inpainting. It demonstrates the performance of this paper in inpainting different diseases of murals.

#INPAINT CRACK CRACK#

Finally, the segmental loss function and its training method are improved.The experimental results show that the results of using signal-to-noise ratio (PSNR), structural similarity (SSIM), and mean square error (MSE) on epitaxial mask, crack mask, random small mask, and random large mask are better than other methods. Secondly, the model combines spatial and channel attention with multiscale feature aggregation to change the mapping network structure and improve the inpainting accuracy.

#INPAINT CRACK GENERATOR#

Firstly, an improved residual prior and attention mechanism is added to the generator module to preserve the image structure. Therefore, this paper presents an inpainting model based on dual-attention multiscale feature aggregation and an improved generator. The generative adversarial network is, recently, a more effective method. Traditional deep learning inpainting causes information loss and generates irrational structures. Due to the scarcity of samples and the variety of damage, the image features are scattered and partially sparse, and the colors are less vivid than in other images. Therefore, the need for digital inpainting is increasing to save time and costs. Traditional mural inpainting takes a long time and requires experts to draw it manually. As the only underground mural in the collection, the tomb murals are subject to damage due to temperature, humidity, and foundation settlement changes.

Inpaint crack