Denoising High Resolution Multispectral Images Using Deep Learning Approach

Ojha, Utkarsh; Garg, Ankur

doi:10.1109/icmla.2016.0156

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…The family of algorithms that fit to a known model, such as least-square fitting, usually produce more reliable results at the cost of requiring that some initial parameters are known beforehand. There are also non-fitting methods, such as centroid [24] and learning-based [25,26] techniques, which require no or minimal information to be known a priori, making them potentially faster and more robust, albeit they are frequently not applicable in all noise regimes [27]. Although these are successful strategies in their own right, the resulting solutions are susceptible to noise and especially in the low SNR regime can suffer considerably from bias and poor precision.…”

Section: Methodsmentioning

confidence: 99%

SNR enhancement in brillouin microspectroscopy using spectrum reconstruction

Xiang¹,

Foreman²,

Török³

2020

Biomed. Opt. Express

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Brillouin imaging suffers from intrinsically low signal-to-noise ratios (SNR). Such low SNRs can render common data analysis protocols unreliable, especially for SNRs below ∼ 10. In this work we exploit two denoising algorithms, namely maximum entropy reconstruction (MER) and wavelet analysis (WA), to improve the accuracy and precision in determination of Brillouin shifts and linewidth. Algorithm performance is quantified using Monte-Carlo simulations and benchmarked against the Cramér-Rao lower bound. Superior estimation results are demonstrated even at low SNRS (≥ 1). Denoising was furthermore applied to experimental Brillouin spectra of distilled water at room temperature, allowing the speed of sound in water to be extracted. Experimental and theoretical values were found to be consistent to within ±1% at unity SNR.

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Section: Methodsmentioning

confidence: 99%

SNR enhancement in brillouin microspectroscopy using spectrum reconstruction

Xiang¹,

Foreman²,

Török³

2020

Biomed. Opt. Express

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“…The network was trained on large datasets comprising noisefree as well as noisy images, and it was found to work well for the task of denoising. Ojha and Garg [36] used autoencoders for denoising high-resolution multispectral images. It was shown in this paper that after training the model on a large set of noisy and denoised images, results comparable to non-local means algorithm are obtained in a significantly lesser amount of time.…”

Section: Introductionmentioning

confidence: 99%

Stacked autoencoders based machine learning for noise reduction and signal reconstruction in geophysical data

Bhowick,

Gupta,

Maiti

et al. 2019

Preprint

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Autoencoders are neural network formulations where the input and output of the network are identical and the goal is to identify the hidden representation in the provided datasets. Generally, autoencoders project the data nonlinearly onto a lower dimensional hidden space, where the important features get highlighted and interpretation of the data becomes easier. Recent studies have shown that even in the presence of noise in the input data, autoencoders can be trained to reconstruct the noisefree component of the data from the reduced-dimensional hidden space.In this paper, we explore the application of autoencoders within the scope of denoising geophysical datasets using a data-driven methodology. The autoencoder formulation is discussed, and a stacked variant of deep autoencoders is proposed. The proposed method involves locally training the weights first using basic autoencoders, each comprising a single hidden layer. Using these initialized weights as starting points in the optimization model, the full autoencoder network is then trained in the second step. The applicability of denoising autoencoders has been demonstrated on a basic mathematical example and several geophysical examples. For all the cases, autoencoders are found to significantly reduce the noise in the input data.

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Image Denoising using Autoencoders : Denoising noisy imgaes by removing noisy pixels/grains from natural images using Deep learning and autoencoders techniques

Kulkarni,

Patil,

et al. 2023

2023 IEEE 8th International Conference for Convergence in Technology (I2CT)

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Denoising High Resolution Multispectral Images Using Deep Learning Approach

Cited by 5 publications

References 8 publications

SNR enhancement in brillouin microspectroscopy using spectrum reconstruction

SNR enhancement in brillouin microspectroscopy using spectrum reconstruction

Stacked autoencoders based machine learning for noise reduction and signal reconstruction in geophysical data

Image Denoising using Autoencoders : Denoising noisy imgaes by removing noisy pixels/grains from natural images using Deep learning and autoencoders techniques

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