CNN-based method for blotches and scratches detection in archived videos

Yous, Hamza; Serir, Amina; Yous, Sofiane

doi:10.1016/j.jvcir.2019.02.005

Cited by 13 publications

(7 citation statements)

References 15 publications

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“…All simulations were carried out in MATLAB (MathWorks, Natick, MA, USA). Let's discuss first data set for test one, this case is to establish the robustness of the proposed method, two types of artifacts i.e., Strip lines and Blotches [40], [41] and one types of noise i.e., Salt & Pepper [42], [43] are added in the images at varying levels ( Fig. 3).…”

Section: Preliminaries For Noisy Data Set Generationmentioning

confidence: 99%

Spatially Adaptive Image Denoising via Enhanced Noise Detection Method for Grayscale and Color Images

2020

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Keeping in view the variety of the applications, image denoising still remains the unexplored territory for the researchers. There are many pros and cons in existing denoising algorithms. The two prime cons of image denoising algorithms are (i) Over and under detection of noisy pixels (ii) Low performance at high noise levels. So, in order to overcome these existing issues, a spatially adaptive image denoising via enhanced noise detection method (SAID-END) is proposed for grayscale and color images. The denoising is achieved using a two-stage sequential algorithm, the first stage ensures accurate noise estimation by eliminating over and under detection of noisy pixels. The second stage performs image restoration by considering non-noisy pixels in estimation of the original pixel value. To enhance the accuracy while denoising high-density impulse noise and artifacts, both noise estimation and restoration stages are using a spatially adaptive window (window expands to spatially connected area), the size of the window depends upon the noise level in the vicinity of the reference noisy pixel. The two stages of the proposed method are referred to as (i) Enhanced adaptive noise detection (ii) Non-corrupted pixel sensitive adaptive image restoration. The proposed method is evaluated by two test steps to ensure its versatility and robustness. In the first step, the proposed method is tested on a wide standard data set of color and grayscale images affected by impulse noise and artifacts. The results of proposed method are compared with well-known methods compatible for denoising impulse noise and artifacts. In the second step, the results of proposed method are compared with the recent state of the art algorithms for traditional test images. The result shows that the proposed method outperforms the existing denoising methods when applied to grayscale and color images.

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Section: Preliminaries For Noisy Data Set Generationmentioning

confidence: 99%

Spatially Adaptive Image Denoising via Enhanced Noise Detection Method for Grayscale and Color Images

2020

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“…CNN, as an excellent feature extraction module, becomes an indispensable part for image classification. They have achieved a great success on kinds of tasks, such as weather recognition [19], industrial components defect detection [20], video surveillance [21] and so on.…”

Section: Introductionmentioning

confidence: 99%

A Light-Weighted CNN Model for Wafer Structural Defect Detection

Chen

Han

et al. 2020

IEEE Access

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Silicon wafer is the raw material of semiconductor chip. It is important and challenging to research a fast and accurate method of identifying and classifying wafer structural defects. To this end, we present a novel detection method in terms of the convolution neural networks (CNN), which achieve more than 99% detection accuracy. Due to the wafer images are not available by open datasets, a set of imaging acquisition system is designed to capture wafer images. Digital image preprocessing technology is utilized to split a wafer image into thousands of silicon grain images. The proposed model, called WDD-Net, uses depthwise separable convolutions and global average pooling to reduce parameters and calculations, adopts multiple 1 * 1 standard convolutions to increase the network depth. Specifically, two types of CNN models, VGG-16 and MobileNet-v2, are adopted for comparative analysis. Using the aforementioned three models, the comparative experiments are implemented on data sets that consisting of more than ten thousand grain images. The experimental results show that compared with VGG-16 and MobileNet-v2, the detection speed of the WDD-Net is 105.6FPS, which is 5 times faster. The model size of the WDD-Net is 307KB, which is much smaller than the other two. Furthermore, the WDD-Net directly completes the data collection and defect detection process through the local computing equipment, which is suitable for edge computing. INDEX TERMS Image classification, neural networks, semiconductor manufacturing, machine learning.

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“…Image destabilization occurs when recording a video with the handheld camera. However, one of the more frequent and annoying deteriorations is blotches [3]. e blotches are usually caused by dust or chemical deterioration that accumulates over time on the films and are characterized by being irregular and approximately constant intensity, dispersed in different spatial locations, creating a temporary discontinuity.…”

Section: Introductionmentioning

confidence: 99%

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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CNN-based method for blotches and scratches detection in archived videos

Cited by 13 publications

References 15 publications

Spatially Adaptive Image Denoising via Enhanced Noise Detection Method for Grayscale and Color Images

Spatially Adaptive Image Denoising via Enhanced Noise Detection Method for Grayscale and Color Images

A Light-Weighted CNN Model for Wafer Structural Defect Detection

Blotch Detection in Archive Films Based on Visual Saliency Map

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