Convolved Feature Vector Based Adaptive Fuzzy Filter for Image De-Noising

Habib, Muhammad; Hussain, Ayyaz; Rehman, Eid; Muzammal, Syeda Mariam; Cheng, Benmao; Aslam, Muhammad; Jilani, Syeda Fizzah

doi:10.3390/app13084861

Cited by 7 publications

(1 citation statement)

References 42 publications

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“…Other approaches use fuzzy logic tools to measure magnitude differences, providing an adaptive framework to distinguish between noise and data. For example, the adaptive fuzzy filter proposed in [21] uses a noise-detection mechanism to select a small portion of input image pixels and convolves them with a set of weighted kernels to create a layer of convolved feature vectors. The feature vectors are then fed into a fuzzy inference system, where fuzzy membership degrees and a reduced set of fuzzy rules play a crucial role in categorizing pixels as either noise-free, associated with edges, or noisy.…”

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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A New Filter Based on Sliding Mode Method with Discrete‐Time Implementation for Noise Attenuation and Differentiation

Zhao,

2024

Advcd Theory and Sims

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This paper presents a novel filter based on the sliding mode method for filtering noise and extracting reliable signals from noisy signals by enhancing Levant's sliding mode filter. Specifically, the proposed sliding mode filter takes advantage of the generalized signum function to adjust the gain in different regions of phase space, thereby decreasing the overshoot during the response phase. Additionally, the discrete‐time implementation of the proposed sliding mode filter is achieved by utilizing the implicit‐Euler algorithm, which effectively eliminates chattering in the output. The effectiveness of the presented sliding mode filter is substantiated via numerical simulation cases.

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An efficient lightweight network for image denoising using progressive residual and convolutional attention feature fusion

Tiantian,

Hu,

Guan

2024

Sci Rep

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While deep learning has become the go-to method for image denoising due to its impressive noise removal capabilities, excessive network depth often plagues existing approaches, leading to significant computational burdens. To address this critical bottleneck, we propose a novel lightweight progressive residual and attention mechanism fusion network that effectively alleviates these limitations. This architecture tackles both Gaussian and real-world image noise with exceptional efficacy. Initiated through dense blocks (DB) tasked with discerning the noise distribution, this approach substantially reduces network parameters while comprehensively extracting local image features. The network then adopts a progressive strategy, whereby shallow convolutional features are incrementally integrated with deeper features, establishing a residual fusion framework adept at extracting encompassing global features relevant to noise characteristics. The process concludes by integrating the output feature maps from each DB and the robust edge features from the convolutional attention feature fusion module (CAFFM). These combined elements are then directed to the reconstruction layer, ultimately producing the final denoised image. Empirical analyses conducted in environments characterized by Gaussian white noise and natural noise, spanning noise levels 15–50, indicate a marked enhancement in performance. This assertion is quantitatively corroborated by increased average values in metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index for Color images (FSIMc), outperforming the outcomes of more than 20 existing methods across six varied datasets. Collectively, the network delineated in this research exhibits exceptional adeptness in image denoising. Simultaneously, it adeptly preserves essential image features such as edges and textures, thereby signifying a notable progression in the domain of image processing. The proposed model finds applicability in a range of image-centric domains, encompassing image processing, computer vision, video analysis, and pattern recognition.

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Convolved Feature Vector Based Adaptive Fuzzy Filter for Image De-Noising

Cited by 7 publications

References 42 publications

Genetic Programming to Remove Impulse Noise in Color Images

Genetic Programming to Remove Impulse Noise in Color Images

A New Filter Based on Sliding Mode Method with Discrete‐Time Implementation for Noise Attenuation and Differentiation

An efficient lightweight network for image denoising using progressive residual and convolutional attention feature fusion

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