Pattern-Based Reconstruction of K-Level Images From Cutsets

We present a pattern-based approach for reconstructing a K-level image from cutsets, dense samples taken along a family of lines or curves in two- or three-dimensional space, which break the image into blocks, each of which is typically reconstructed independently of the others. The pattern-based approach utilizes statistics of human segmentations to generate a codebook of patterns, each of which represents a pair of a block boundary specification and the corresponding pattern in the block interior. We develop the approach for rectangular cutset topologies and show that it can be extended to general periodic sampling topologies. We also show that, for bilevel cutset reconstruction, the pattern-based can be combined with the previously proposed cutset-MRF approach to substantially reduce the size of the codebook with a slight increase in reconstruction error. In addition, we present an algorithm for segmenting the cutset samples of an original grayscale or color image, followed by reconstruction of the full segmentation field via the pattern-based approach. Experimental results show that the proposed approaches outperform the cutset-MRF approaches in terms of both reconstruction error rate and perceptual quality. Moreover, this is accomplished without any side information about the structure of the block interior. Systematic comparisons of the performance of different sampling topologies are also provided.

Automated summary

This study presents a pattern-based approach for image reconstruction using statistics and human segmentation. Experimental results demonstrate its superior performance in terms of reconstruction error rate and perceptual quality.