Kyung-tai Kim

Film line scratch detection using texture and shape information

A scratch detection and restoration is very important, as scratches are the most common form of degradation of old films. The goal of the current study is to develop a fully automated system that can detect all types of scratch with a low computational cost. This is achieved by defining the texture and shape properties from spatial domain, then using these for scratch detection. The proposed method involves two procedures: (1) the input image is divided into scratches and non-scratches using a neural network (NN)-based texture classifier and (2) some false alarms are removed by shape filtering using a morphological filter with new structuring elements defined based on the shape characteristics of scratches. To assess the validity of the proposed method, experiments were performed with several films, and the results showed that its performance was superior to that of other method.

Automatic Film Line Scratch Removal System based on Spatial Information

In this paper, an automatic film line scratch removal system is developed that can deal with all kind of scratches. For this we use the spatial information of scratches: the scratch in old films has lower or higher brightness than neighboring pixels in its vicinity and usually appears as a vertically long thin line. Our system consists of scratch detection and scratch restoration. The scratches of various types are detected by neural network based texture classifier and morphology-based shape filter and then the degraded regions are restored using bilinear interpolation. To assess the validity of the proposed method, it has been tested with all kinds of scratches, and then experimental results show that the proposed approach leads to not only robust but also efficient scratch restoration.

Film line scratch detection using neural network and morphological filter

This paper presents a scratch detection method that automatically detects all kinds of scratches from each frame in old films. Generally, the scratch in old films has lower or higher brightness than neighboring pixels in its vicinity and it usually appears as a vertically long thin line. The proposed method is designed from these characteristics of a scratch, thus it consists of two major modules: a neural network-based texture classifier and a morphology-based shape filter with multiple structuring elements. First, the NN-based texture classifier divides the input image into scratch regions and non-scratch regions using the texture property of the scratch. Secondly, the morphology-based shape filter confirms the classified scratch region with structuring elements which is designed based on the shape characteristics of scratches. To assess the validity of the proposed method, the experiments have been performed on several old films and an animation, then the results confirms that the proposed method can detect all kinds of scratches and have the potential to be applied to the commercial systems.

Automatic Restoration of Scratch in Old Archive

This paper presents scratch restoration method that can deal with scratches of various lengths and widths in old film. The proposed method consists of detection and reconstruction. The detection is performed using texture and shape properties of the scratches: first, each pixel is classified as scratches and non-scratches using a neural network (NN)-based texture classifier, and then some false alarms are removed by shape filtering. Thereafter, the detected region is reconstructed. Here, the reconstruction is formulated as energy minimization problem, thus genetic algorithm is used as optimization algorithm. The experimental result with well-known old films showed the effectiveness of the proposed method.

Genetic algorithm-based reconstruction of old films corrupted by scratches and blotches

This paper presents a method for restoring old films corrupted by scratches and blotches. In the proposed method, the reconstruction problem is formulated in a Bayesian framework, where a priori and likelihood are used to model the smoothness within the original images and between the original and observed images, respectively. As the proposed model uses no prior knowledge of scratches and blotches, it can be used to restore various types of degradation. In addition, to minimize the energy function derived in the proposed model, a modified version of DGAs is used, where chromosomes are evolved according to regionally assigned properties, thereby improving the convergence to stable solutions. The proposed restoration method was tested using well-known old films and artificially corrupted films, and the experimental results showed that the proposed algorithm is very efficient and the quality of the images reconstructed using the proposed method is competitive with that restored using existing film restoration methods.

Outdoor situation recognition using support vector machine for the blind and the visually impaired

Traffic intersections are most dangerous situations for the pedestrian, in particular the blind or the visually impaired person. In this paper, we present a novel method for automatically recognizing the situations where a user stands on, to help safe mobility of the visually impaired in their travels. Here, the situation means the place type where a user is standing on, which is classified as sidewalk, roadway and intersection. The proposed method is performed by three steps: ROIs extraction, feature extraction and classification. The ROIs corresponding to the boundaries between sidewalks and roadways are first extracted using Canny edge detector and Hough transform. From those regions, features are extracted using Fourier transform, and they fed into two SVMs. One SVM is trained to learn the textural properties of sidewalk and the other is for intersection. On online stage, these two SVMs are hierarchically performed; the current situation is first categorized as sidewalks and others, then it is re-categorized as intersections and others. The proposed method was tested with about 500 outdoor images, then it showed the accuracy of 93.9 %.

Reconstruction of degraded images using genetic algoritm for archive film restoration

A film restoration has been received considerable attention by many researchers, to support multimedia service of high quality. So far many techniques have been developed, however, such techniques do not permit the reconstruction of all kinds of degradation, because they have been developed based on their own specific environments and assumptions. This paper represents automatic restoration method for various type of degradation region. For this, we develop a stochastic method in MRF-MAP (Markov random field - maximum a posteriori) framework, where the restoration problem is formulated as the minimization problem of the posteriori energy function. Then, to minimize the energy function, we use distributed genetic algorithms (DGAs) that effectively deal with combinatorial problems. To assess the validity of the proposed method, it was tested on natural old films and artificially degraded films, and the results were compared with other methods. Then, the results show that the proposed method is superior to other methods.