Removal of ‘Salt &amp; Pepper’ noise from color images using adaptive fuzzy technique based on histogram estimation

Roy, Amarjit; Manam, Lalit; Laskar, Rabul Hussain

doi:10.1007/s11042-020-09107-x

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…The NAFSM filter uses two-stage processing specialized for salt-and-pepper noise detection and removal, preserving strong edges through spatial averaging. A recent contribution in a similar vein is the adaptive fuzzy filter based on histogram estimation (AFHE) [23]. The AFHE filter dynamically adjusts the window size based on local noise densities using fuzzybased criteria.…”

Section: Related Workmentioning

confidence: 99%

Genetic Programming to Remove Impulse Noise in Color Images

Fajardo-Delgado,

Rodríguez-González,

Sandoval-Pérez

et al. 2023

Applied Sciences

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This paper presents a new filter to remove impulse noise in digital color images. The filter is adaptive in the sense that it uses a detection stage to only correct noisy pixels. Detecting noisy pixels is performed by a binary classification model generated via genetic programming, a paradigm of evolutionary computing based on natural biological selection. The classification model training considers three impulse noise models in color images: salt and pepper, uniform, and correlated. This is the first filter generated by genetic programming exploiting the correlation among the color image channels. The correction stage consists of a vector median filter version that modifies color channel values if some are noisy. An experimental study was performed to compare the proposed filter with others in the state-of-the-art related to color image denoising. Their performance was measured objectively through the image quality metrics PSNR, MAE, SSIM, and FSIM. Experimental findings reveal substantial variability among filters based on noise model and image characteristics. The findings also indicate that, on average, the proposed filter consistently exhibited top-tier performance values for the three impulse noise models, surpassed only by a filter employing a deep learning-based approach. Unlike deep learning filters, which are black boxes with internal workings invisible to the user, the proposed filter has a high interpretability with a performance close to an equilibrium point for all images and noise models used in the experiment.

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Section: Related Workmentioning

confidence: 99%

Genetic Programming to Remove Impulse Noise in Color Images

Fajardo-Delgado,

Rodríguez-González,

Sandoval-Pérez

et al. 2023

Applied Sciences

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“…In [12], an Adaptive fuzzy lter based on histogram estimation (AFHE) has been proposed to remove the salt-and-pepper noise from color images. This method's processing window size is adapted according to local noise densities through the fuzzy-based criterion [12]. In [13], numerous digital lters such as mean lter, median lter, wiener lter, and adaptive lter are applied to remove different levels of Gaussian, salt-and-pepper, and speckle noise is added separately into the MR images.…”

Section: A Review Of the Backgroundmentioning

confidence: 99%

Section: A Review Of the Backgroundmentioning

confidence: 99%

An efficient method for high-density multimodal salt-and-pepper noise removal of MRI images

Ebrahimnejad

Naghsh

2022

Preprint

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Medical image noise reduction is a significant and challenging area in image processing. A new adaptive window-based solution for the removal of high-density multimodal salt-and-pepper noise of the brain MRI images is proposed in this paper. In this efficient method, for each pixel of the noisy input image, an adaptive n x n window is considered in the neighborhood of that pixel, where n depends on the noise value. The higher the noise density, the larger the window size in which healthy pixels are found. If they are not noisy, the pixels of this window are weighted according to their distance from the desired pixel. The greater the distance, the less weight they gain. Then, the weighted sum of the neighboring pixels is averaged, and the noisy pixel replaces with the resulting value. To evaluate the proposed method against multimodal salt-and-pepper noise, which simultaneously appears in an image from 1–98%, 208 images from seven MRI databases are applied. The results show the excellent performance of the proposed method. The mean Peak Signal to Noise Ratio (PSNR) of whole databases is 29.3465. As a preprocessing step, the efficient proposed method shows highly accurate results on the brain MRI images. After applying the noise removal method, the quality and the Structural Similarity (SSIM) increased. In this study, in addition to removing multimodal noise in an image, noise with a specific density (single mode) in each image is also removed with similar or better results.

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