Trainable post-processing method to reduce false alarms in the detection of small blotches of archive films

Licsár, Attila; Czúni, László; Szirányi, Tamás

doi:10.1109/icip.2005.1530117

Cited by 4 publications

(7 citation statements)

References 11 publications

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“…Since it is very difficult to construct an explicit blotch model for a given film and using general models might be useless considering the large variety of archive films we propose an adaptive classification by training [10].…”

Section: False Alarm Reduction With Trainable Blotch Classificationmentioning

confidence: 99%

“…In our previous work [10], the silhouette of defects were manually painted for each pixel of the image. This process was time consuming so we developed a technique that speeds up this process.…”

Section: False Alarm Reduction With Trainable Blotch Classificationmentioning

confidence: 99%

“…The idea of the blotch classification was first introduced in our previous work [10] that is now completed with improved region of interest (ROI) based motion estimation, blotch detection, feature extraction, classification, training and evaluation methods.…”

Section: Introductionmentioning

confidence: 99%

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Trainable blotch detection on high resolution archive films minimizing the human interaction

Licsár

Szirányi

Czúni

2007

Machine Vision and Applications

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Film archives are continuously in need of automatic restoration tools to accelerate the correction of film artifacts and to decrease the costs. Blotches are a common type of film degradation and their correction needs a lot of manual interaction in traditional systems due to high false detection rates and the huge amount of data of high resolution images. Blotch detectors need reliable motion estimation to avoid the false detection of uncorrupted regions. In case of erroneous detection, usually an operator has to remove the false alarms manually, which significantly decreases the efficiency of the restoration process. To reduce manual intervention, we developed a two-step false alarm reduction technique including pixel-and object-based methods as post-processing. The proposed pixel-based algorithm compensates motion, decreasing false alarms at low computational cost, while the following object based method further reduces the residual false alarms by machine learning techniques. We introduced a new quality metric for detection methods by measuring the required amount of manual work after the automatic detection. In our novel evaluation technique, the ground truth is collected from digitized archive sequences where defective pixel positions are detected in an interactive process.

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Section: False Alarm Reduction With Trainable Blotch Classificationmentioning

confidence: 99%

Section: False Alarm Reduction With Trainable Blotch Classificationmentioning

confidence: 99%

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Trainable blotch detection on high resolution archive films minimizing the human interaction

Licsár

Szirányi

Czúni

2007

Machine Vision and Applications

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show abstract

“…[21][22][23][24], which regards scratch as multiplicative noise. This change is based on both physical and chemical causes of scratch.…”

Section: Hybrid Noise Modelmentioning

confidence: 99%

“…The blotch artifact can be very bright, very dark or even semi-transparent, as instanced in Figure 3. All of these properties increase the difficulty of removing blotch [16,[21][22][23][24][25].…”

Section: The Analysis Of Noise Scratch and Blotchmentioning

confidence: 99%

Automatic movie restoration based on wave atom transform and nonparametric model

Zhai

Chen

et al. 2012

EURASIP J. Adv. Signal Process.

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This article presents a new scheme for movie de-scratching, de-noising and de-blotching based on wave atom transform and nonparametric model. According to the analysis of the noise, scratches and blotches in the film, we establish a hybrid model for movie signal. In the proposed model, we model scratch as directional additive noise, which can be represented effectively by a new multi-scale analysis tool called wave atom transform. Based on wave atoms, de-noising and de-scratching are combined in a nested thresholding framework which can suppress both homogeneous noise and structural artifacts. On the other hand, blotch is described as multiplicative noise in the model, which can be restored by a "detecting and inpainting" strategy. We use the inter-and intra-frame discontinuity of blotch to detect them and then restore them by kernel regression method. Combining the processing operations above together, we design an effective automatic movie restoration system. Experimental results show that the proposed method not only achieves excellent performances even for seriously damaged movies but also has low computational complexity compared with the existing methods.

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Blotch Detection Based on Texture Matching and Adaptive Multi-threshold

Wei

Zhang

Hao

et al. 2009

2009 Fifth International Conference on Image and Graphics

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Blotch is a typical artifact in old films and the detection of them is an important step in film restoration. The existing simplified rank-ordered difference detector achieves higher detection rates by reducing the value of threshold. However, the corresponding higher number of false alarms is undesirable. To maximize the ratio between correct detections and false alarms, this paper proposes an improved blotch detector based on adaptive multithreshold. According to different objects of blotches, the proposed detector can achieve the most appropriate threshold by convergence confinement. Meanwhile, texture matching is introduced to avoid the possible deviation caused by motion vector estimation in the regions with blotches. Performance evaluation is taken to the image sequences with both real blotches and artificially corrupted ones. The experimental results indicate higher correct detection rates and fewer false alarms simultaneously.

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Trainable post-processing method to reduce false alarms in the detection of small blotches of archive films

Cited by 4 publications

References 11 publications

Trainable blotch detection on high resolution archive films minimizing the human interaction

Trainable blotch detection on high resolution archive films minimizing the human interaction

Automatic movie restoration based on wave atom transform and nonparametric model

Blotch Detection Based on Texture Matching and Adaptive Multi-threshold

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