A Frequency-Domain Approach with Learnable Filters for Image Classification

Stuchi, José Augusto; Canto, Natália Gil; Boccato, Levy; Attux, Romis

doi:10.2139/ssrn.4333420

Cited by 4 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such quality assessment was performed by a convolutional neural network (CNN), which was previously trained, based on factors including blurriness, exposure, presence of artifacts, media opacities, or incorrect fields of view. 16 Images classified as gradable after the automatic quality evaluation underwent further automatic DR detection by 2 different DL systems, whose specific details are outlined below.…”

Section: Methodsmentioning

confidence: 99%

Automated Identification of Different Severity Levels of Diabetic Retinopathy Using a Handheld Fundus Camera and Single-Image Protocol

Malerbi,

Nakayama,

Melo

et al. 2024

Ophthalmology Science

View full text Add to dashboard Cite

Section: Methodsmentioning

confidence: 99%

Automated Identification of Different Severity Levels of Diabetic Retinopathy Using a Handheld Fundus Camera and Single-Image Protocol

Malerbi,

Nakayama,

Melo

et al. 2024

Ophthalmology Science

View full text Add to dashboard Cite

“…From Table 2, we can see the comparison between linear filtering and nonlinear filtering. [32]. Filtering is then applied in the frequency domain, followed by an inverse transform to return to the time or spatial domain [33].…”

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

“…In summary, this study highlighted the significant contributions of frequency-domain approaches and learnable frequency filters in image classification problems. The developed methods demonstrated high performance in various application areas, making an important contribution to the literature [16].…”

Section: Introductionmentioning

confidence: 96%

Fourier-Based Image Classification Using CNN

Dağı,

Gökçay,

Tora

2024

JSTER

View full text Add to dashboard Cite

Recently, Convolutional Neural Networks (CNNs) have achieved remarkable success in computer vision, image processing and image processing tasks. Traditional CNN models work directly with spatial domain images. On the other hand, images obtained with Fast Fourier Transform (FFT) represent the Frequency domain and provide an advantage in computational cost by reducing potential calculation complexity. This study uses FFT converted images as input to the CNN algorithm to increase image classification and recognition accuracy and investigates the effects of this. The study begins with a comprehensive review of the foundations and features of FFT. It assumes that by converting the input images from the Spatial domain to the Frequency domain, the input image can be learned more efficiently and better results can be achieved in terms of performance by studying the most important features in the Frequency domain. To evaluate the effectiveness of this assumption, CIFAR-10, MNIST-Digits and MNIST-Fashion datasets were used. As a result, it has been shown that FFT-based preprocessing can improve classification accuracy, especially in cases where the datasets contain high-frequency noise, and it has shown different results in different datasets. Therefore, it is thought that the effect of FFT preprocessing varies depending on the datasets.

show abstract

A Frequency-Domain Approach with Learnable Filters for Image Classification

Cited by 4 publications

References 0 publications

Automated Identification of Different Severity Levels of Diabetic Retinopathy Using a Handheld Fundus Camera and Single-Image Protocol

Automated Identification of Different Severity Levels of Diabetic Retinopathy Using a Handheld Fundus Camera and Single-Image Protocol

Filtering Methods for Biomedical Image Denoising

Fourier-Based Image Classification Using CNN

Contact Info

Product

Resources

About