Adaptive Non-linear Filtering Technique for Image Restoration

Satpathy, S. K.; Panda, Sampad Kumar; Nagwanshi, Kapil Kumar; Nayak, Sanghamitra; Ardil, Cemal

doi:10.48550/arxiv.2204.09302

Cited by 6 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They gauge the MVDF's efficacy using a variety of reference colour images compromised by impulsive noise, with intensity ranging from 3% to 20%. The outcomes of the experiments notably indicate that the MVDF surpasses the effectiveness of both the VDF and the Generalized VDF (GVDF) across various parameters including PSNR, NCD (Normalized Colour Difference), and execution time for the denoised image.In the context of article [7], a decision-based nonlinear algorithm is proposed for the eradication of artifacts such as band lines, drop lines, marks, band losses, and impulses from images. The algorithm adeptly carries out simultaneous operations-detecting corrupted pixels and evaluating new pixels for substitution of the corrupted ones.…”

Section: Literature Surveymentioning

confidence: 99%

A Novel Technique for Restoration of Images Subjected to Variable Noise Distribution

Kalaiah J B, Dr. S.N Chandrashekara

2023

tjjpt

View full text Add to dashboard Cite

Image restoration plays a pivotal role in numerous applications across diverse fields due to its profound significance in enhancing the quality and interpretability of images. Whether in medical diagnostics, scientific research, surveillance, or historical preservation, the importance of image restoration cannot be overstated. By addressing issues such as noise, blur, and other forms of degradation, this process contributes to the preservation of vital information within images. This research focuses on enhancing image-denoising techniques by employing the Ladner-Fischer Adder (LFA) method. The study explores the effectiveness of LFA in mitigating varying noise distributions, particularly Salt and Pepper Noise (SPN) and Gaussian noise. Through detailed experimentation and analysis, the paper evaluates the performance of LFA-based Median Filtering (LFA-MF) and Finite Impulse Response (LFA-FIR) filtering methods. The results demonstrate substantial improvements in Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index Measure (SSIM) values for both noise types. Notably, LFA-FIR consistently outperforms LFA-MF in terms of denoising efficacy, highlighting its potential for image restoration in the presence of complex noise patterns. Moreover, the research investigates the ASIC synthesis of LFA, examining parameters such as area, power consumption, and delay across different technology nodes. The findings emphasize the potential of LFA for robust image denoising under diverse noise conditions, offering insights into its applicability and performance in real-world scenarios.

show abstract

Section: Literature Surveymentioning

confidence: 99%

A Novel Technique for Restoration of Images Subjected to Variable Noise Distribution

Kalaiah J B, Dr. S.N Chandrashekara

2023

tjjpt

View full text Add to dashboard Cite

show abstract

“…Non-Linear Filtering [28]: (i) Non-linear filters, unlike linear filters, apply non-linear operations to modify or process data. Examples include median filters and morphological filters [29].…”

Section: Remove Unwanted Interference or Noise At Specific Frequenciesmentioning

confidence: 99%

Filtering Methods for Biomedical Image Denoising

Zhao

2023

Preprint

View full text Add to dashboard Cite

In this paper, the filtering method of biomedical image denoising is described comprehensively. Firstly, it introduces the biomedical image denoising, describes the relationship between biomedical image denoising and medical care, introduces the filtering methods, the filtering methods of biomedical image denoising, the challenges encountered by the current filtering methods, and other application fields of filtering methods. Firstly, the background of biomedical image denoising is introduced. Biomedical image denoising is a challenge. Different imaging modes have different noise characteristics, and noise levels can vary greatly depending on the specific application. Secondly, it describes that biomedical image denoising plays an important role in medical care, and the biomedical image directly affects the patient's diagnosis, treatment plan and the overall quality of medical care service. Then the filtering method is introduced in detail, describing the core concepts and related features of linear filtering, nonlinear filtering and frequency domain filtering, and then focusing on the adaptive filtering method, describing the characteristics, conditions of use, common algorithms and advantages of adaptive filtering method. Then the filter methods of biomedical image denoising are introduced, and the core concepts of Gaussian filter, median filter, total variation denoising and Wiener filter are introduced respectively. Then, the challenges encountered by filtering methods are described, such as the accurate selection of filters, the balance between noise reduction and image detail preservation are introduced. Finally, the application of filtering method in other fields is mentioned, such as audio processing, speech recognition and so on. In summary, this paper comprehensively expounds the denoising and filtering methods of biomedical images, the filtering methods of medical image denoising, the relationship between medical image denoising and medical care, and the challenges encountered by filtering methods.

show abstract