Image Integration with Local Linear Model Using Demosaicing Algorithm

Abstract

Recovering picture from corrupted observations necessary for several real-world applications. During this paper, we propose a unified framework to perform progressive image recovery supported hybrid graph Laplacian regularized regression. We first construct a multiscale illustration of the target image by Laplacian pyramid, then more and more recover the degraded image within the scale area from coarse to fine so the sharp edges and texture will be eventually recovered. On one hand, among every scale, a graph Laplacian regularization model represented by implicit kernel is learned, that at the same time minimizes the smallest amount sq. error on the measured samples and preserves the geometrical structure of the image information area. In this procedure, the intrinsic manifold structure is expressly considered exploitation each measured and unmeasured samples, and the nonlocal selfsimilarity property is used as a fruitful resource for abstracting aprioriknowledge of the photographs. On the other hand, between 2 sequential scales, the projected model is extended to a projected high-dimensional feature area through explicit kernel mapping to explain the interscale correlation, in which the native structure regularity is learned and propagated from coarser to finer scales. During this manner, the projected algorithmic rule gradually recovers additional and additional image details and edges, which couldn't be recovered in previous scale. We have a tendency to take a look at our algorithm on one typical image recovery task: impulse noise removal. Experimental results on benchmark take a look at pictures demonstrate that the projected technique achieves higher performance than progressive algorithms.