Improving salt and pepper noise removal using a fuzzy mathematical morphology-based filter

González-Hidalgo, Manuel; Massanet, Sebastià; Mir, Arnau; Ruiz-Aguilera, Daniel

doi:10.1016/j.asoc.2017.11.030

Cited by 42 publications

(21 citation statements)

References 20 publications

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“…Mathematical morphology is an arithmetical tool for image analysis based on morphological structural elements. It has a huge influence on the theory and technology of image processing, especially on shape and structure analysis, which has been widely applied in noise removal [47,48], feature extraction [49,50] and image enhancement [51,52]. Mathematical morphology is specialized in edge processing for its capability of global description.…”

Section: ) Morphologicalmentioning

confidence: 99%

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Luo

Fang

Liu

et al. 2020

IEEE Trans. Instrum. Meas.

339

111

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Automated computer-vision-based defect detection has received much attention with the increasing surface quality assurance demands for the industrial manufacturing of flat steels. This paper attempts to present a comprehensive survey on surface defect detection technologies by reviewing about 120 publications over the last two decades for three typical flat steel products of con-casting slabs, hot-and cold-rolled steel strips. According to the nature of algorithms as well as image features, the existing methodologies are categorized into four groups: Statistical, spectral, model-based and machine learning. These literatures are summarized in this review to enable easy referral to suitable methods for diverse application scenarios in steel mills. Realization recommendations and future research trends are also addressed at an abstract level.

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Section: ) Morphologicalmentioning

confidence: 99%

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Luo

Fang

Liu

et al. 2020

IEEE Trans. Instrum. Meas.

339

111

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“…Several state-of-the-art methods for salt-and-pepper denoising [51,52] consist of first detecting the noisy pixels in the image, and then applying a denoising filter at the noise locations. In the filtering stage, fuzzy morphological operators such as alternate sequential filters have proven to be very effective [52]. In this regard, the ability to learn from data the parameters of these filters leading to the best denoising would be very useful.…”

Section: Image Denoisingmentioning

confidence: 99%

Deep morphological networks

Franchi

Fehri²,

Yao

2020

Pattern Recognition

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Mathematical morphology provides powerful nonlinear operators for a variety of image processing tasks such as filtering, segmentation, and edge detection. In this paper, we propose a way to use these nonlinear operators in an end-toend deep learning framework and illustrate them on different applications. We demonstrate on various examples that new layers making use of the morphological non-linearities are complementary to convolution layers. These new layers can be used to integrate the non-linear operations and pooling into a joint operation. We finally enhance results obtained in boundary detection using this new family of layers with just 0.01% of the parameters of competing state-of-the-art methods.

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“…Inspired by [12], an improved noise‐detection technique, called boundary discriminative noise detection by elimination, was proposed in [13], which retains the advantages of the filter in [12] while suppressing noises effectively. To improve the performance, the composite application of median filter was proposed, such as [15, 16]. An extremely fast adaptive high‐performance filter (FAHPF) proposed in [15] removes noises by an overlapping median filter embedded with the mean filter.…”

Section: Related Workmentioning

confidence: 99%

“…A weighted median filter [5–7] tried to improve the standard median filter by weighting the neighbour pixels, but all the pixels in the whole image were processed uniformly without local treatment. So, further improvements were contributed in literatures [5, 8–33] in order to effectively remove impulse noises. Nonetheless, all these works even introduce new disadvantages while dealing with the drawback in the previous methods, or obtain little improvement to the previous methods, and thus are not necessarily effective.…”

Section: Introductionmentioning

confidence: 99%