  Image inpaiting is an important task in image processing and vision. In this
paper, we develop a general method for patch-based image inpainting by
synthesizing new textures from existing one. A novel framework is introduced to
find several optimal candidate patches and generate a new texture patch in the
process. We form it as an optimization problem that identifies the potential
patches for synthesis from an coarse-to-fine manner. We use the texture
descriptor as a clue in searching for matching patches from the known region.
To ensure the structure faithful to the original image, a geometric constraint
metric is formally defined that is applied directly to the patch synthesis
procedure. We extensively conducted our experiments on a wide range of testing
images on various scenarios and contents by arbitrarily specifying the target
the regions for inference followed by using existing evaluation metrics to
verify its texture coherency and structural consistency. Our results
demonstrate the high accuracy and desirable output that can be potentially used
for numerous applications: object removal, background subtraction, and image
retrieval.

Computer Vision and Pattern Recognition