Deep neural networks (DNNs) appear to be a solution for the classification of polarimetric synthetic aperture radar (PolSAR) data in that they outperform classical supervised classifiers under condition sufficient training samples. The design classifier is challenging because DNNs can easily overfit due limited remote sensing samples and unavoidable noisy labels. In this article, softmax loss s...

Abstract Given an image filter $${{\varvec{y}}}={{\varvec{f}}}\,({{\varvec{x}}})$$ y = f ( x ) , where $${{\varvec{x}}}$$ and $${{\varvec{y}}}$$ are input output images, respectively, rev...

conclusion various filters are needed to improve the image quality. in this study, seven different filters were implemented and investigated on both noisy and noise-free images. background developmental dysplasia of the hip (ddh) can be detected with ultrasonography (usg) images. however, the accuracy of this method is dependent on the skill of the radiologist. radiologists measure the hip join...

Background: Nowadays, image de-noising plays a very important role in medical analysis applications and pre-processing step. Many filters were designed for image processing, assuming a specific noise distribution, so the images which are acquired by different medical imaging modalities must be out of the noise. Objectives: This study has focused on the sequence filters which are selected ...

In allusion to the losses of image detail and texture structure information during image de-noising process, an image de-noising algorithm based on non-related dictionary learning is proposed in this paper. Firstly, this algorithm is adopted to obtain selfadaption redundant dictionary for the noisy image through the dictionary learning algorithm; secondly, HOG features and gray-level statistica...

in the current iris recognition systems, noise removing step is only used to detect noisy parts of the iris region and features extracted from there will be excluded in matching step. whereas depending on the filter structure used in feature extraction, the noisy parts may influence relevant features. to the best of our knowledge, the effect of noise factors on feature extraction has not been c...

The spatial and scale space domain techniques are used independently to detect edges of the noisy images. When noise density surpasses a limit, classical operators are unable to detect the edges. The frequency domain filtering for edge detection in a noisy scenario is inadequate due to Fourier’s global behavior. Wavelet analysis for noisy images also reveals dominance of noisy pixels over the e...

Periodic noises are unwished and spurious signals that create repetitive pattern on images and decreased the visual quality. Firstly, this paper investigates various methods for reducing the effects of the periodic noise in digital images. Then an adaptive optimum notch filter is proposed. In the proposed method, the regions of noise frequencies are determined by analyzing the spectral of noisy...