Archive Film Restoration Based on Spatiotemporal Random Walks

Abstract

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.

A simple computational framework for defect detection system with orthogonal polynomials transcoded coefficients

“…Hence automatic defect identification, with the help of digital image proceesing scheme, is a natural alternative. In this direction, defect detection in application areas such as automated manufacturing [2,3], textile fabric [4][5][6][7][8][9][10][11], film industry [12][13][14], wood [15][16][17], construction industry [18], Printed Circuit Board (PCB) ( [19][20][21], wafer [22][23][24][25], solar cells [25][26][27][28], paper industry [29], leather [30], food processing [31,32], and rails [33] are reported in the literature. These image analysis techniques, designed for defect detection, are implemented either on non-textured surface like paper and glass materials or homogeneously textured surfaces like textile or on structural patterns like semiconductor wafer dies and Liquid Crystal Display (LCD).…”

Local binary patterns on triangular meshes: Concept and applications

“…It has been originally introduced for representing 2D textures in still images, but its computational simplicity and discriminative power attracted the attention of the image processing and pattern recognition community for other different tasks. Rapidly, LBP has found applications in visual inspection [3,4], remote sensing [5][6][7], face recognition [8][9][10][11], facial expression recognition [12], and motion analysis [13,14]. However, the LBP-based methods developed so far operate either on photometric information provided by 2D color images or on geometric information in 2D depth images.…”

The Mesh-LBP: A Framework for Extracting Local Binary Patterns From Discrete Manifolds

“…Its computational simplicity and discriminative power attracted the attention of the image processing and pattern recognition community, and rapidly it has found other applications in visual inspection [3], [4], remote sensing [5]- [7], face recognition [8]- [11], facial expression recognition [12], and motion analysis [13], [14]. However, all the LBP-based methods developed so far operate either on photometric information provided by 2D color images or on geometric information in 2D depth images.…”