Gradient based restoration of coal mine images obtained by underground wireless transmissions

Curvature-driven diffusion (CDD) principles were used to develop a novel gradient based image restora- tion algorithm. The algorithm fills in blocks of missing data in a wireless image after transmission through the network. When images are transmitted over fading channels, especially in the severe circum- stances of a coal mine, blocks of the image may be destroyed by the effects of noise. Instead of using com- mon retransmission query protocols the lost data is reconstructed by using the adaptive curvature-driven diffusion (ACDD) image restoration algorithm in the gradient domain of the destroyed image. Missing blocks are restored by the method in two steps: In step one, the missing blocks are filled in the gradient domain by the ACDD algorithm; in step two, and the image is reconstructed from the reformed gradients by solving a Poisson equation. The proposed method eliminates the staircase effect and accelerates the convergence rate. This is demonstrated by experimental results.

Improved shape from shading without initial information

The number of constraints imposed on the sur- face, the light source, the camera model and in particular the initial information makes shape from shading （SFS） very dif- ficult for real applications. There are a considerable number of approaches which require an initial data about the 3D ob- ject such as boundary conditions （BC）. However, it is difficult to obtain these information for each point of the object Edge in the image, thus the application of these approaches is lim- ited. This paper shows an improvement of the Global View method proposed by Zhu and Shi [1]. The main improvement is that we make the resolution done automatically without any additional information on the 3D object. The method in- volves four steps. The first step is to determine the singular curves and the relationship between them. In the second step, we generate the global graph, determine the sub-graphs, and determine the partial and global configuration. The proposed method to determine the convexity and the concavity of the singular curves is applied in the third step. Finally, we apply the Fast-Marching method to reconstruct the 3D object. Our approach is successfully tested on some synthetic and real im- ages. Also, the obtained results are compared and discussed with some previous methods.

Denoising method of X-ray phase contrast DR image for TRISO-coated fuel particles

TRISO （tristructural-isotropic） fuel is a type of micro fuel particles used in high-temperature gas-cooled reactors （HTGRs）. Among the quality evaluation methods for such particles, inqine phase contrast imaging technique （PCI） is more feasible for nondestructive measurement. Due to imaging hardware limitations, high noise level is a distinct feature of PCI images, and as a result, the dimensional measurement accuracy of TRISO-coated fuel particles decreases. Therefore, we propose an improved denoising hybrid model named as NL P-M model which introduces non-local theory and retains the merits of the Perona-Malik （P-M） model. The improved model is applied to numerical simulation and practical PCI images. Quanti- tative analysis proves that this new anisotropic diffusion model can preserve edge or texture information effectively, while ruling out noise and distinctly decreasing staircasing artifacts. Especially during the process of coating layer thickness measurement, the NL P-M model makes it easy to obtain continuous contours without noisy points or fake contour segments, thus enhancing the measurement accuracy. To address calculation complexity, a graphic processing unit （GPU） is adopted to realize the acceleration of the NL P-M denoising.