A New Adaptive Filter to Remove Impulsive Noise in Color Images

Atitallah, Ahmed Ben

doi:10.1002/tee.23594

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…The advantage of the directional-distance filter is the capability to remove the noise and repair the images without introducing a significant distortion. The drawback is that these direction based filters present a high computational complexity [44]. In the research conducted by Atitallah et al [44], an optimized field-programmable gate array (FPGA) (hardware) was designed in combination with the adaptive vector directional distance filter (AVDDF) for removing noise from the images in real-time.…”

Section: Directional-distance Filtersmentioning

confidence: 99%

Digital Filtering Techniques Using Fuzzy-Rules Based Logic Control

Yin,

Hadjiloucas

2023

J. Imaging

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This paper discusses current formulations based on fuzzy-logic control concepts as applied to the removal of impulsive noise from digital images. We also discuss the various principles related to fuzzy-ruled based logic control techniques, aiming at preserving edges and digital image details efficiently. Detailed descriptions of a number of formulations for recently developed fuzzy-rule logic controlled filters are provided, highlighting the merit of each filter. Fuzzy-rule based filtering algorithms may be designed assuming the tailoring of specific functional sub-modules: (a) logical controlled variable selection, (b) the consideration of different methods for the generation of fuzzy rules and membership functions, (c) the integration of the logical rules for detecting and filtering impulse noise from digital images. More specifically, we discuss impulse noise models and window-based filtering using fuzzy inference based on vector directional filters as associated with the filtering of RGB color images and then explain how fuzzy vector fields can be generated using standard operations on fuzzy sets taking into consideration fixed or random valued impulse noise and fuzzy vector partitioning. We also discuss how fuzzy cellular automata may be used for noise removal by adopting a Moore neighbourhood architecture. We also explain the potential merits of adopting a fuzzy rule based deep learning ensemble classifier which is composed of a convolutional neural network (CNN), a recurrent neural networks (RNN), a long short term memory neural network (LSTM) and a gated recurrent unit (GRU) approaches, all within a fuzzy min-max (FMM) ensemble. Fuzzy non-local mean filter approaches are also considered. A comparison of various performance metrics for conventional and fuzzy logic based filters as well as deep learning filters is provided. The algorhitms discussed have the following advantageous properties: high quality of edge preservation, high quality of spatial noise suppression capability especially for complex images, sound properties of noise removal (in cases when both mixed additive and impulse noise are present), and very fast computational implementation.

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Section: Directional-distance Filtersmentioning

confidence: 99%

Digital Filtering Techniques Using Fuzzy-Rules Based Logic Control

Yin,

Hadjiloucas

2023

J. Imaging

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“…Then, the time is computed by subtracting the start time from the end time and dividing by the number of timer ticks per second. Besides, image quality is assessed using commonly used objective metrics [29,30], such as the Peak Signal to Noise Ratio (PSNR) and the Normalized Color Difference (NCD).…”

Section: Figure 7 Sw/hw Vdf Design Resourcesmentioning

confidence: 99%

Accelerated FPGA-Based Vector Directional Filter for Real-Time Color Image Denoising with Enhanced Performance

Alanazi¹

2023

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This paper presents an accelerated implementation of the Vector Directional Filter (VDF) on a Field Programmable Gate Array (FPGA) for real-time denoising of color images. The VDF effectively suppresses noise while preserving edges and fine details, making it ideal for a range of applications such as satellite and multispectral biomedical imaging. However, the filter's high computational complexity poses challenges for real-time processing. Existing solutions either fail to meet real-time execution requirements or compromise image quality through hardware implementation approximations. To overcome these challenges, we first model the VDF using C/C++ programming, and subsequently design an efficient floating-point hardware architecture employing the High-Level Synthesis (HLS) flow. Optimal directives are selected using the Xilinx Vivado HLS tool. The VDF architecture is then integrated as a coprocessor with the Cortex-A53 hardcore processor in the XCZU9EG FPGA. To enhance data bandwidth between software and hardware components, three Direct Memory Access (DMAs) units are utilized to transfer three image lines in parallel. Furthermore, internal memory is implemented on the XCZU9EG FPGA, providing increased flexibility for managing the restored image. The VDF Software/Hardware (SW/HW) design's robustness and accuracy are validated through experimental studies on the ZCU102 board. Our design accelerates the filtering process by 21 times, maintaining visual quality and effectively removing noise from color images compared to the VDF SW design. Additionally, our solution outperforms existing approaches in terms of filtered image quality and processing time, showing a 24% improvement in the worst-case scenario.

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“…In this paper, we propose a new approach to refine the search and identification of noisy pixels. Considering a 5 × 5 window, our proposed MLVMF uses a modified Laplacian filter to observe the intensity variations around each pixel of the image with a rotation step of π/8 while the classic Laplacian filters often use rotation steps of π/2 or π/4 [ 42 , 43 , 44 , 45 ]. Reducing the angle of rotation around the pixel makes it possible to take advantage of additional information about its neighborhood.…”

Section: Introductionmentioning

confidence: 99%

New Real-Time Impulse Noise Removal Method Applied to Chest X-ray Images

et al. 2022

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In this paper, we propose a new Modified Laplacian Vector Median Filter (MLVMF) for real-time denoising complex images corrupted by “salt and pepper” impulsive noise. The method consists of two rounds with three steps each: the first round starts with the identification of pixels that may be contaminated by noise using a Modified Laplacian Filter. Then, corrupted pixels pass a neighborhood-based validation test. Finally, the Vector Median Filter is used to replace noisy pixels. The MLVMF uses a 5 × 5 window to observe the intensity variations around each pixel of the image with a rotation step of π/8 while the classic Laplacian filters often use rotation steps of π/2 or π/4. We see better identification of noise-corrupted pixels thanks to this rotation step refinement. Despite this advantage, a high percentage of the impulsive noise may cause two or more corrupted pixels (with the same intensity) to collide, preventing the identification of noise-corrupted pixels. A second round is then necessary using a second set of filters, still based on the Laplacian operator, but allowing focusing only on the collision phenomenon. To validate our method, MLVMF is firstly tested on standard images, with a noise percentage varying from 3% to 30%. Obtained performances in terms of processing time, as well as image restoration quality through the PSNR (Peak Signal to Noise Ratio) and the NCD (Normalized Color Difference) metrics, are compared to the performances of VMF (Vector Median Filter), VMRHF (Vector Median-Rational Hybrid Filter), and MSMF (Modified Switching Median Filter). A second test is performed on several noisy chest x-ray images used in cardiovascular disease diagnosis as well as COVID-19 diagnosis. The proposed method shows a very good quality of restoration on this type of image, particularly when the percentage of noise is high. The MLVMF provides a high PSNR value of 5.5% and a low NCD value of 18.2%. Finally, an optimized Field-Programmable Gate Array (FPGA) design is proposed to implement the proposed method for real-time processing. The proposed hardware implementation allows an execution time equal to 9 ms per 256 × 256 color image.

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A New Adaptive Filter to Remove Impulsive Noise in Color Images

Cited by 3 publications

References 19 publications

Digital Filtering Techniques Using Fuzzy-Rules Based Logic Control

Digital Filtering Techniques Using Fuzzy-Rules Based Logic Control

Accelerated FPGA-Based Vector Directional Filter for Real-Time Color Image Denoising with Enhanced Performance

New Real-Time Impulse Noise Removal Method Applied to Chest X-ray Images

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