Blotch detection in archived video based on regions matching

Yous, Hamza; Serir, Amina

doi:10.1109/isivc.2016.7894019

Cited by 5 publications

(12 citation statements)

References 8 publications

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“…Since almost every nation restores historical archive films in general, there is no public dataset. Different methods have been developed for blotch detection applications [6][7][8][9][10][11][12]. e study in [1] can be used to obtain more detailed information about research studies in this field.…”

Section: Related Studiesmentioning

confidence: 99%

“…Here, the most classical approaches and recent studies will be taken into consideration. ese studies have been conducted on the basis of temporal discontinuity, different intensities, and shape characteristics of blotches [9][10][11][12]. e performance of these studies varies depending on the characteristics of blotches and the efficient handling of the complexity of archive film content.…”

Section: Related Studiesmentioning

confidence: 99%

“…In fact, the AR and MRF models, which have more computational complexity, produce almost the same performance as the SDI [9,12]. e use of textural information in the region of interest has been suggested in addition to these region-based methods in blotch detection problems [1,11]. In the first step of the former approach carried out by Yous and Serir, candidate regions were extracted by time-spatial segmentation.…”

Section: Related Studiesmentioning

confidence: 99%

“…In the first step of the former approach carried out by Yous and Serir, candidate regions were extracted by time-spatial segmentation. In the second step, the blotches in the candidate regions were determined based on the gradient map [11]. Yous and Serir proposed a new detection method in their later approach, using the feature extraction approaches in [1].…”

Section: Related Studiesmentioning

confidence: 99%

“…e degraded regions are then concealed using information from defectfree regions. Many of the blotch detections in the literature have focused on the possibility of the blotches occurring at different spatial positions in successive frames [9][10][11][12]. erefore, both previous and next frames for the current frame are generally used as a reference for detecting the blotches.…”

Section: Introductionmentioning

confidence: 99%

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Blotch Detection in Archive Films Based on Visual Saliency Map

Aydin

Dіzdaroğlu

2020

Complexity

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Degradations frequently occur in archive films that symbolize the historical and cultural heritage of a nation. In this study, the problem of detection blotches commonly encountered in archive films is handled. Here, a block-based blotch detection method is proposed based on a visual saliency map. The visual saliency map reveals prominent areas in an input frame and thus enables more accurate results in the blotch detection. A simple and effective visual saliency map method is taken into consideration in order to reduce computational complexity for the detection phase. After the visual saliency maps of the given frames are obtained, blotch regions are estimated by considered spatiotemporal patches—without the requirement for motion estimation—around the saliency pixels, which are subjected to a prethresholding process. Experimental results show that the proposed block-based blotch detection method provides a significant advantage with reducing false alarm rates over HOG feature (Yous and Serir, 2017), LBP feature (Yous and Serir, 2017), and regions-matching (Yous and Serir, 2016) methods presented in recent years.

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Section: Related Studiesmentioning

confidence: 99%

Section: Related Studiesmentioning

confidence: 99%

Section: Related Studiesmentioning

confidence: 99%

Section: Related Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Blotch Detection in Archive Films Based on Visual Saliency Map

Aydin

Dіzdaroğlu

2020

Complexity

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Correl-Net: Defect Segmentation in Old Films Using Correlation Networks

Renaudeau

Seng

Carlier

et al. 2023

Lecture Notes in Computer Science

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The old film restoration process involves many operations, one of which is the ability to identify defects that altered the film. This operation can be formulated as a binary segmentation problem and solved using state-of-the-art segmentation networks such as DeepLab v3+ or NAS-FPN. While being very powerful at describing the spatial characteristics of defects, these methods fail to take into account the fact that defects are also temporal anomalies. We therefore propose an architecture that builds on the correlation layer introduced in FlowNet to compensate for motion and eliminate potential false positives, features that look like defects but can be tracked over multiple images and are actually part of the scene. We also introduce a self-supervised pre-training process of the network, which precedes a fine-tuning phase to specifically adapt the detector to each film. Results show that our architecture, while being more compact and less resource-consuming than state-of-the-art methods, achieves higher precision and recall.

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