Shi Jin-fei

Novel algorithm for image mosaic using linear texture match

To realize the real-time mosaic of image sequences in large scale mechanical parts with linear texture, this paper proposes a novel algorithm of image mosaic based on linear texture matching (LTM). The traditional algorithm of image mosaic consumes so much time that it can not be used on production lines. The proposed algorithm not only greatly reduces computing time but also enhances anti-disturbance force. The performance of the proposed technique is evaluated by processing some practical image sequences. To address problems that are caused by the relative rotation between two sequential partial images, the image sequences is adjusted to keep the linear texture consistent with the abscissas axis of gray values. Then the new algorithm focuses on the translation according to the characteristics of gray values of the image sequences. The result demonstrates that the proposed algorithm greatly enhances the efficiency of image match, which can satisfy the requirement of online image mosaic.

Noise-insensitive and edge-preserving resolution upconversion scheme for digital image based on the spatial general autoregressive model

This study proposes an edge-preserving image interpolation algorithm for both clean images and polluted images. First, the structure of an image window is learnt adaptively by using a spatial general autoregressive (SGAR) model that is a uniform expression for both linear and non-linear autoregressive (AR) models. Parameters of the SGAR model are estimated in a moving window in the input low-resolution image by using the robust generalised M-estimator. Next, the interpolation model is established from the learnt model and a new feedback mechanism in accordance with the residual sum of squares minimisation principle. Finally, the gradient simulated annealing algorithm is used to solve the interpolation model, which can rapidly converge to the global optimum in probability with the help of gradient information. Experiments have been performed using worldwide datasets to evaluate the performance of the authors method. The results demonstrate that their method is superior to a recent AR model-based method and is bicubic, especially when images are polluted by noise such as Gaussian noise, Poisson noise, impulse noise, or a combination of these.