Pyramid-based texture analysis/synthesis
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Image quilting for texture synthesis and transfer
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Missing data correction in still images and image sequences
Proceedings of the tenth ACM international conference on Multimedia
Texture Synthesis by Non-Parametric Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Processing And Analysis: Variational, Pde, Wavelet, And Stochastic Methods
Image Processing And Analysis: Variational, Pde, Wavelet, And Stochastic Methods
Automatic restoration of old motion picture films using spatiotemporal exemplar-based inpainting
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
MCMC for joint noise reduction and missing data treatment indegraded video
IEEE Transactions on Signal Processing
On missing data treatment for degraded video and film archives: a survey and a new Bayesian approach
IEEE Transactions on Image Processing
Region filling and object removal by exemplar-based image inpainting
IEEE Transactions on Image Processing
Interpolation of missing data in image sequences
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
| Hi-index | 0.00 |
We propose a novel restoration method for defects and missing regions in video sequences, particularly in application to archive film restoration. Our statistical framework is based on random walks to examine the spatiotemporal path of a degraded pixel, and uses texture features in addition to intensity and motion information traditionally used in previous restoration works. The degraded pixels within a frame are restored in a multiscale framework by updating their features (intensity, motion and texture) at each level with reference to the attributes of normal pixels and other defective pixels in the previous scale as long as they fall within the defective pixel's random walk-based spatiotemporal neighbourhood. The proposed algorithm is compared against two state-of-the-art methods to demonstrate improved accuracy in restoring synthetic and real degraded image sequences.