Denoising algorithm of OCT images via sparse representation based on noise estimation and global dictionary.
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TLDR
The proposed denoising algorithm through sparse representation via sparse representation based on noise estimation and global dictionary exhibits satisfying results in terms of speckle-noise reduction as well as edge preservation, at a reduced computational cost.Abstract:
Optical coherence tomography (OCT) is a high-resolution and non-invasive optical imaging technology, which is widely used in many fields. Nevertheless, OCT images are disturbed by speckle noise due to the low-coherent interference properties of light, resulting in significant degradation of OCT image quality. Therefore, a denoising algorithm of OCT images via sparse representation based on noise estimation and global dictionary is proposed in this paper. To remove noise and improve image quality, the algorithm first constructs a global dictionary from high-quality OCT images as training samples and then estimates the noise intensity for each input image. Finally, the OCT images are sparsely decomposed and reconstructed according to the global dictionary and noise intensity. Experimental results indicate that the proposed algorithm efficiently removes speckle noise from OCT images and yield high-quality images. The denoising effect and execution efficiency are evaluated based on quantitative metrics and running time, respectively. Compared with the mainstream adaptive dictionary denoising algorithm in sparse representation and other denoising algorithms, the proposed algorithm exhibits satisfying results in terms of speckle-noise reduction as well as edge preservation, at a reduced computational cost. Moreover, the final denoising effect is significantly better for sets of images with significant variations in noise intensity.read more
Citations
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Hybrid-structure network and network comparative study for deep-learning-based speckle-modulating optical coherence tomography
TL;DR: Zhang et al. as discussed by the authors proposed a deep-learning-based speckle-modulating OCT based on a hybrid-structure network, residual-dense-block U-Net generative adversarial network (RDBU-Net GAN), and further conducted a comprehensively comparative study.
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Speckle attenuation for optical coherence tomography images using the generalized low rank approximations of matrices.
TL;DR: In this paper , a despeckling method is proposed to effectively reduce the speckle noise in OCT images using the generalized low rank approximations of matrices (GLRAM).
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A generative adversarial network with multi-scale convolution and dilated convolution res-network for OCT retinal image despeckling
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Optical coherence tomography image despeckling based on edge feature-guided higher-order singular value decomposition
TL;DR: Wang et al. as mentioned in this paper proposed a feature-guided higher-order singular value decomposition (HOSVD)-based method to reduce the speckle noise in OCT images while preserving local structures.
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OCT image denoising algorithm based on discrete wavelet transform and spatial domain feature fusion
TL;DR: In this article, an OCT image denoising fusion based on discrete wavelet transform and spatial domain feature weighting is proposed to solve the problem of speckle noise, which blurs the structural information of the image such as layer structure and lesion point.
References
More filters
Journal ArticleDOI
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Journal ArticleDOI
Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Journal ArticleDOI
Matching pursuits with time-frequency dictionaries
Stéphane Mallat,Zhifeng Zhang +1 more
TL;DR: The authors introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions, chosen in order to best match the signal structures.
Journal ArticleDOI
$rm K$ -SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
TL;DR: A novel algorithm for adapting dictionaries in order to achieve sparse signal representations, the K-SVD algorithm, an iterative method that alternates between sparse coding of the examples based on the current dictionary and a process of updating the dictionary atoms to better fit the data.
Journal ArticleDOI
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
Michael Elad,Michal Aharon +1 more
TL;DR: This work addresses the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image, and uses the K-SVD algorithm to obtain a dictionary that describes the image content effectively.