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

Yu, Cheng‐Ching; Hou, Li-Zhong

doi:10.1109/tce.2022.3175796

Cited by 9 publications

(2 citation statements)

References 60 publications

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“…This type of noise can significantly degrade the quality of images and poses various challenges in real-world applications. For example, the impulse noise in dashboard camera footage under low-light conditions [7] can lead to the misinterpretation of videos, making it challenging to accurately identify vehicles involved in incidents. Additionally, impulse noise is a kind of ordinary noise in medical imaging (X-rays, MRIs, and CT scans) [8] that can result in the misinterpretation or disappearance of critical details important in diagnosis.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

Genetic Programming to Remove Impulse Noise in Color Images

Fajardo-Delgado,

Rodríguez-González,

Sandoval-Pérez

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…These images are mainly degraded due to the defects that arise during the image sensing and acquisition processes. This illposed problem affects ultrasound imaging and MRI in medical science, SAR images in remote sensing, and a variety of other scientific and industrial images [1] [40] [41]. Most of the methods available in the literature aim to remove these degradations from images, but doing so without disturbing the image's essential features has always been difficult.…”

Section: Introductionmentioning

confidence: 99%

A Two Phase Ultrasound Image De-speckling Framework by Nonlocal Means on Anisotropic Diffused Image Data

Thakur

Khan

Sharma

2023

IJCAI

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Image de-speckling is one of the most challenging issues in multimedia imaging systems. All of the available speckle noise reduction filters are nearly noise reduction capable, but they fail to restore subtle features such as low grey level edges and fine details against a low contrast background. Nonlocal mean filtering and anisotropic diffusion are two popular and effective methods for image despeckling while preserving detail. This paper presents a two-phase ultrasound image de-speckling framework by utilizing the capability of the non-local mean filtering method for de-speckling and edge preservation on anisotropic diffused images. The prior image smoothing along with edge preservation and contrast enhancement by anisotropic diffusion is carried out in the first phase, which is then followed by the non-local means method for de-speckling and edge sharpening in the next phase. The degree of speckle noise attenuation is measured on low-contrast standard and ultrasound images and compared to state-of-the-art and advanced anisotropic diffusion techniques and non-local means methods. The percentage improvement of PSNR over the existing methods is found to be in the range of 2.06% to 46.68%. The experimental results show that the proposed method is capable of reducing noise and preserving edges better than existing speckle reduction filters.Povzetek: Predstavljen je dvofazni pristop za odstranjevanje šuma iz ultrazvočnih slik, ki se je v primerjavi z obstoječimi filtri za zmanjšanje šuma izkazal za boljšega v ohranjanju robov in zmanjševanju šuma.

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