Mixed Impulse Noise Research Articles

Realization of a Real-Time Image Denoising System for Dashboard Camera Applications

Noise interference during the acquisition of digital images can severely degrade image quality, particularly for images captured under low-light conditions; however, the removal of image noise requires sophisticated digital image processing systems. This study presents a hardware-based solution to real-time image denoising using an existing algorithm designed for the removal of mixed impulse noise (salt-and-pepper and random-valued impulse noise), while preserving image edge details and image borders, without the need for additional computation time or memory capacity. Note that mixed impulse noise is typical of most real-world situations, such as the video noise associated with dashboard cameras. The proposed design was implemented using 180 nm complementary metal-oxide-semiconductor (CMOS) technology, consuming only 21.7 mW when operated at 200 MHz. This operating frequency allows the proposed chip to process noisy video streams with resolution of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1920\mathbf {\times }1080$ </tex-math></inline-formula> at 60 frames per second in real time. In terms of image restoration, the proposed algorithm achieved image quality on par with that achieved using software simulation. We also demonstrated the efficacy of the proposed scheme in denoising noisy video images from a dashboard camera.

A Bio-inspired Parallel-Framework Based Multi-gene Genetic Programming Approach to Denoise Biomedical Images

The occurrence of noise is a common problem in biomedical imaging applications. The denoising of corrupted biomedical images is a challenging task. In this paper, we present a biologically inspired parallel-framework based multi-gene genetic programming (MGGP) approach for denoising biomedical images from mixed impulse noise. Our biologically inspired approach has achieved an improved denoising performance by exploiting its parallel framework of multiple genes modeling capability in noise detection and removal stages. In the detection stage, we developed MGGP-based noise detector using rank-ordered and robust statistical features to effectively locate the corrupted pixels and generate noise map. In the noise removal stage, the detected noisy pixels are denoised by developing a bio-inspired MGGP-based estimator using statistical features of only noise-free pixels in their neighborhood. Extensive experimentation is carried out to demonstrate the robust performance of the proposed approach on diverse types of biomedical images corrupted with different noise densities. As a test case, we evaluated the performance of the proposed bio-inspired approach for benchmark biomedical images of Algae, C05c, Celulas, Crm04280, Crm05210, Nemacb1, Nemacl2, MRI, X-ray, Heart and microscopic images of fungal spores causing wheat rust. The proposed parallel-framework based bio-inspired approach has demonstrated an improved performance over other existing conventional and bio-inspired learning approaches.

Switching degenerate diffusion PDE filter based on impulselike probability for universal impulse noise removal

In this paper, a switching degenerate diffusion partial differential equation filter (SDDPDE) is developed by introducing the switching operators for reducing all kinds of impulse noise, and especially for images having a mixture of salt-and-pepper impulse noise and random-valued impulse noise which is a shortage for most of the existing filtering models. Our SDDPDE consists of the coarse and fine filtering stages. In the coarse filtering stages, the switching operator depends on a simple noise detector. In the fine filtering stages, we introduce the notion of impulselike probability, and the switching operator depends on both a simple noise detector and impulselike probability. Our SDDPDE will denoise noise pixels detected by the coarse detector while further modify the so-called noise-free pixels according to impulselike probability. The main advantages of our SDDPDE over published approaches are its simplicity and universality. In addition, we demonstrate the performance of our SDDPDE via application to three standard test images, corrupted by salt-and-pepper impulse noise, random-valued impulse noise and mixed impulse noise with high-noise levels, and the comparison with the other well-known filters. Experimental results show that our SDDPDE achieves high peak signal-to-noise ratio and better visual effect.

Genetic Fuzzy Filter Based on MAD and ROAD to Remove Mixed Impulse Noise

In this paper, we propose a genetic fuzzy image filtering based on rank-ordered absolute differences (ROAD) and median of the absolute deviations from the median (MAD). The proposed method consists of three components, including fuzzy noise detection system, fuzzy switching scheme filtering, and fuzzy parameters optimization using genetic algorithms (GA) to perform efficient and effective noise removal. Our idea is to utilize MAD and ROAD as measures of noise probability of a pixel. Fuzzy inference system is used to justify the degree of which a pixel can be categorized as noisy. Based on the fuzzy inference result, the fuzzy switching scheme that adopts median filter as the main estimator is applied to the filtering. The GA training aims to find the best parameters for the fuzzy sets in the fuzzy noise detection. By the experimental results, the proposed method has successfully removed mixed impulse noise in low to medium probabilities, while keeping the uncorrupted pixels less affected by the median filtering. It also surpasses the other methods, either classical or soft computing-based approaches to impulse noise removal, in MAE and PSNR evaluations.

Switching Bilateral Filter With a Texture/Noise Detector for Universal Noise Removal

In this paper, we propose a switching bilateral filter (SBF) with a texture and noise detector for universal noise removal. Operation was carried out in two stages: detection followed by filtering. For detection, we propose the sorted quadrant median vector (SQMV) scheme, which includes important features such as edge or texture information. This information is utilized to allocate a reference median from SQMV, which is in turn compared with a current pixel to classify it as impulse noise, Gaussian noise, or noise-free. The SBF removes both Gaussian and impulse noise without adding another weighting function. The range filter inside the bilateral filter switches between the Gaussian and impulse modes depending upon the noise classification result. Simulation results show that our noise detector has a high noise detection rate as well as a high classification rate for salt-and-pepper, uniform impulse noise and mixed impulse noise. Unlike most other impulse noise filters, the proposed SBF achieves high peak signal-to-noise ratio and great image quality by efficiently removing both types of mixed noise, salt-and-pepper with uniform noise and salt-and-pepper with Gaussian noise. In addition, the computational complexity of SBF is significantly less than that of other mixed noise filters.

Universal Impulse Noise Filter Based on Genetic Programming

In this paper, we present a novel method for impulse noise filter construction, based on the switching scheme with two cascaded detectors and two corresponding estimators. Genetic programming as a supervised learning algorithm is employed for building two detectors with complementary characteristics. The first detector identifies the majority of noisy pixels. The second detector searches for the remaining noise missed by the first detector, usually hidden in image details or with amplitudes close to its local neighborhood. Both detectors are based on the robust estimators of location and scale-median and MAD. The filter made by the proposed method is capable of effectively suppressing all kinds of impulse noise, in contrast to many existing filters which are specialized only for a particular noise model. In addition, we propose the usage of a new impulse noise model-the mixed impulse noise, which is more realistic and harder to treat than existing impulse noise models. The proposed model is the combination of commonly used noise models: salt-and-pepper and uniform impulse noise models. Simulation results show that the proposed two-stage GP filter produces excellent results and outperforms existing state-of-the-art filters.

Adaptive nonlinear filters for simultaneous removal of different kinds of noise in images

A novel adaptive median filter is proposed. It allows the simultaneous removal of a combination of signal-dependent and additive random noise in addition to mixed impulse noise in images, processed in a single filtering pass. The adaptation algorithm is based on the local signal-to-noise ratio. An extension of the class of nonlinear mean filters to adaptive filters is considered. The performance of the adaptive median filter is compared to the commonly used median filter and the nonlinear mean filter.