6533b852fe1ef96bd12aaefb

RESEARCH PRODUCT

Image inpainting using directional wavelet packets originating from polynomial splines

Amir AverbuchPekka NeittaanmäkiMoshe SalhovJonathan HauserValery A. Zheludev

subject

signaalinkäsittelyComputer scienceImage and Video Processing (eess.IV)Inpainting020206 networking & telecommunicationsImage processing02 engineering and technologykuvankäsittelyElectrical Engineering and Systems Science - Image and Video ProcessingWavelet packet decompositionImage (mathematics)Set (abstract data type)Tensor productalgoritmitSignal Processing0202 electrical engineering electronic engineering information engineeringFOS: Electrical engineering electronic engineering information engineeringWaveform020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionElectrical and Electronic EngineeringAlgorithmSoftwareVariable (mathematics)

description

The paper presents a new algorithm for the image inpainting problem. The algorithm is using a recently designed versatile library of quasi-analytic complex-valued wavelet packets (qWPs) which originate from polynomial splines of arbitrary orders. Tensor products of 1D qWPs provide a diversity of 2D qWPs oriented in multiple directions. For example, a set of the fourth-level qWPs comprises 62 different directions. The properties of the presented qWPs such as refined frequency resolution, directionality of waveforms with unlimited number of orientations, (anti-)symmetry of waveforms and windowed oscillating structure of waveforms with a variety of frequencies, make them efficient in image processing applications, in particular, in dealing with the inpainting problem addressed in the paper. The obtained results for this problem are quite competitive with the best state-of-the-art algorithms. The inpainting is implemented by an iterative scheme, which, in essence, is the Split Bregman Iteration (SBI) procedure supplied with an adaptive variable soft thresholding based on the Bivariate Shrinkage algorithm. In the inpainting experiments, performance comparison between the qWP-based methods and the state-of-the-art algorithms is presented.

https://dx.doi.org/10.48550/arxiv.2001.04899