Laplacian-Based Frequency Domain Filter for the Restoration of Digital Images Corrupted by Periodic Noise

Varghese, Justin; Saudia, S.; Tairan, Nasser; Babu, B. Raveendra

doi:10.1109/cjece.2015.2490598

Cited by 18 publications

(13 citation statements)

References 14 publications

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“…These images are selected for comparative analysis of different algorithms according to varying complexities in features, edges and texture details. Similar to other algorithms [7, 29–31, 36–38], the sinusoidal functions that create noisy peaks at frequency spectrum of natural image are used for artificially corrupting images to test the performance of algorithms. These noise functions with strength, a are defined below as

N_{1}

and

N_{2}

N_{1} ()(, i, j) = a * 255 ()(, \begin{matrix} 1em4pt \\ Sin ()(, 1.8 i) + Sin ()(, 1.8 j) + Sin ()(, + j) \\ + Sin ()(, 2.2 i + 2.2 j) + Sin ()(, 1.8 i - 1.8 j) \\ + Sin ()(, i - j) + Sin ()(, 2.2 i - 2.2 j) \end{matrix})

N_{2} ()(, i, j) = a * 255 ()(, \begin{matrix} 1em4pt \\ Sin ()(, 1.1 i + 1.1 j) + Sin ()(, 1.5 i) \\ + Sin ()(, 1.5 j + 2.2 j) + Sin ()(, 1.1 i - 1.1 j) \end{matrix})

Here

()(, i, j)

represents the spatial position.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…For performing quantitative subjective/objective analysis of filtering algorithms, the metrics such as mean absolute error (MAE), peak signal‐to‐noise ratio (PSNR), mean structural similarity index measure (MSSIM) [39] and computation time (CT) in seconds are used. Formulations of MAE, PSNR and MSSIM are as in [7, 29–38]. An effective algorithm needs to produce high‐quality restored outputs with higher PSNR and MSSIM values and lower MAE and CT values.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Chakraborty et al filter [28] used frequency domain histogram based thresholding operation for identifying noisy areas, but this method produces misclassifications in noise detection when the noise strength is high. The noisy peak detection procedures employed by Sur et al filter [29], windowed adaptive switching minimum filter (WASMF) [30], Laplacian‐based frequency domain filter (LFDF) [31], Chakraborty filter [32], Ketenci filter [33] and Ionita filter [34] use static approximation functions but are not adaptive to the noise and image types. Zhou et al [35] proposed a bilateral linear filter operator by incorporating least‐squares regression based noise detection and linear operator based de‐noising.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive Gaussian notch filter for removing periodic noise from digital images

Varghese

Subhash

Subramaniam

et al. 2020

IET Image Processing

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N_{1}

and

N_{2}

N_{1} ()(, i, j) = a * 255 ()(, \begin{matrix} 1em4pt \\ Sin ()(, 1.8 i) + Sin ()(, 1.8 j) + Sin ()(, + j) \\ + Sin ()(, 2.2 i + 2.2 j) + Sin ()(, 1.8 i - 1.8 j) \\ + Sin ()(, i - j) + Sin ()(, 2.2 i - 2.2 j) \end{matrix})

N_{2} ()(, i, j) = a * 255 ()(, \begin{matrix} 1em4pt \\ Sin ()(, 1.1 i + 1.1 j) + Sin ()(, 1.5 i) \\ + Sin ()(, 1.5 j + 2.2 j) + Sin ()(, 1.1 i - 1.1 j) \end{matrix})

Here

()(, i, j)

represents the spatial position.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Gaussian notch filter for removing periodic noise from digital images

Varghese

Subhash

Subramaniam

et al. 2020

IET Image Processing

Self Cite

View full text Add to dashboard Cite

“…In [5], a system for the automatic quantification of wheat ear was proposed based on images acquired by an RGB conventional camera. The algorithm considers 3 steps: (1) detection of abrupt changes by means of Laplacian frequency filter [28], (2) median filtering to smooth the noise, and (3) a segmentation step using Find Maxima. The researchers developed an algorithm through the image analysis system ImageJ [29].…”

Section: Related Workmentioning

confidence: 99%

An Agave Counting Methodology Based on Mathematical Morphology and Images Acquired through Unmanned Aerial Vehicles

Calvario

Alarcón

Dalmau

et al. 2020

Sensors

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Blue agave is an important commercial crop in Mexico, and it is the main source of the traditional mexican beverage known as tequila. The variety of blue agave crop known as Tequilana Weber is a crucial element for tequila agribusiness and the agricultural economy in Mexico. The number of agave plants in the field is one of the main parameters for estimating production of tequila. In this manuscript, we describe a mathematical morphology-based algorithm that addresses the agave automatic counting task. The proposed methodology was applied to a set of real images collected using an Unmanned Aerial Vehicle equipped with a digital Red-Green-Blue (RGB) camera. The number of plants automatically identified in the collected images was compared to the number of plants counted by hand. Accuracy of the proposed algorithm depended on the size heterogeneity of plants in the field and illumination. Accuracy ranged from 0.8309 to 0.9806, and performance of the proposed algorithm was satisfactory.

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“…The visual effects of the restored images obtained with different methods are assessed with the mean structural similarity index measure (MSSIM). Its definition can be found in [32]. The value of MSSIM lies in the interval

(][, 0, 1)

and is equal to 1 when the denoised image is identical to the original clean image.…”

Section: Numerical Experimentsmentioning

confidence: 99%

Image decomposition and denoising using fractional‐order partial differential equations

Bai

Feng

2020

IET image process

View full text Add to dashboard Cite

Laplacian-Based Frequency Domain Filter for the Restoration of Digital Images Corrupted by Periodic Noise

Cited by 18 publications

References 14 publications

Adaptive Gaussian notch filter for removing periodic noise from digital images

Adaptive Gaussian notch filter for removing periodic noise from digital images

An Agave Counting Methodology Based on Mathematical Morphology and Images Acquired through Unmanned Aerial Vehicles

Image decomposition and denoising using fractional‐order partial differential equations

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