Image restoration based on the fast marching method and block based sampling

Toward Image Restoration Based on Neural Network and Similarity Comparison

“…Three damaged standard image data sets were used: Lena image, the two circles image, the window image [13]. The characteristic of these missing image were shown in Fig.…”

“…The orders of missing pixels required for imputing is calculated. These orders are kept into a queue by using the marching method [9][12] [13]. The missing pixels are extracted from this queue after the orders of missing values are calculated.…”

“…This method is an efficient imputation method which is able to preserve the linear structure in the missing area. However, the filling order in the method is random and unreliable and it seems to have the phenomenon of growing garbage [13]. Bertalmio et al [11] proposed an image inpainting method based on smooth propagation of information from the surrounding areas in the isophotes direction.…”

“…The limitation of this algorithm is the blurring produced when inpainting points are thicker than 10-15 pixels [5]. Huan et al [13] introduced an image inpainting algorithm based on a two-step process. In the first step, the filling order of the missing pixels was determined by using the fast marching method.…”

Image Restoration Using Group-based Sparse Representation Technique

“…Uncertainty issues may be resolved within the method of image process by constructing fuzzy characteristic matrix and fuzzy fitness perform, and victimization fuzzy genetic improvement technique. Huan et al [3] have proposed Associate in Nursing economical image in painting algorithmic rule by introducing vital aspects and enhancements admire the filling order of the pixels within the target region and texture synthesis in an exceedingly dynamic looking vary. The algorithmic rule is easy to implement and restores the target regions with visually plausible quality i.e.…”