HINRDNet: A half instance normalization residual dense network for passive millimetre wave image restoration

Huang, Zhigao; Huang, Zhenghua; Hua, Xia; Hu, Hong; Li, Lirong

doi:10.1016/j.infrared.2023.104722

Cited by 4 publications

(2 citation statements)

References 37 publications

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“…In contrast, instance normalization (IN) removes the summation across the batch dimension, and the proposed method utilizes IN to independently compute the mean and variance for each channel. IN has been widely applied to tasks that require high output detail, such as generative adversarial networks (GANs) and image super-resolution [21][22][23], and it can better preserve the timefrequency spectral details of seismic signals [24]. U-Net performs downsampling through max-pooling layers, but Springenberg et al [25] demonstrated through experiments that using convolutions with larger strides instead of pooling for dimensionality reduction can improve the accuracy of image recognition tasks.…”

Section: Generator Network Structurementioning

confidence: 99%

Time–Frequency Domain Seismic Signal Denoising Based on Generative Adversarial Networks

Wei,

Sun,

Zong

2024

Applied Sciences

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Existing deep learning-based seismic signal denoising methods primarily operate in the time domain. Those methods are ineffective when noise overlaps with the seismic signal in the time domain. Time–frequency domain-based deep learning methods are relatively rare and usually employ single loss function, resulting in suboptimal performance on low SNR signals and potential damage to P wave. This paper proposes a method based on generative adversarial networks (GANs). Compared to convolutional neural networks, the discriminator in GANs helps retain more true signal details by judging denoising performance. Additionally, an attention mechanism is introduced to fully extract signal features, and a perceptual loss is employed to evaluate the difference between the denoised result and the target’s high-level features. Experimental results show that this method can effectively improve SNR and ensure that the denoised result is close to the true signal. Furthermore, by comparing DeepDenoiser and ARDU, it is proven that the proposed method achieves better denoising performance, especially for low SNR signals, while causing less damage to the seismic signals.

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Section: Generator Network Structurementioning

confidence: 99%

Time–Frequency Domain Seismic Signal Denoising Based on Generative Adversarial Networks

Wei,

Sun,

Zong

2024

Applied Sciences

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“…Recently, convolution neural network denoiser and regularized term-based iterative method was presented to restore the noise and sharpness [32]. Moreover, deep learning module based on U-Net and half instance normalization block was introduced and this showed the outstanding results in terms of visual perception and evaluation metrics [33].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of L1-Norm-Based Blind Deconvolution after Noise Reduction with Non-Subsampled Contourlet Transform in Light Microscopy Images

Kim,

Kim

2024

Applied Sciences

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Noise and blurring in light microscope images are representative factors that affect accurate identification of cellular and subcellular structures in biological research. In this study, a method for l1-norm-based blind deconvolution after noise reduction with non-subsampled contourlet transform (NSCT) was designed and applied to a light microscope image to analyze its feasibility. The designed NSCT-based algorithm first separated the low- and high-frequency components. Then, the restored microscope image and the deblurred and denoised images were compared and evaluated. In both the simulations and experiments, the average coefficient of variation (COV) value in the image using the proposed NSCT-based algorithm showed similar values compared to the denoised image; moreover, it significantly improved the results compared with that of the degraded image. In particular, we confirmed that the restored image in the experiment improved the COV by approximately 2.52 times compared with the deblurred image, and the NSCT-based proposed algorithm showed the best performance in both the peak signal-to-noise ratio and edge preservation index in the simulation. In conclusion, the proposed algorithm was successfully modeled, and the applicability of the proposed method in light microscope images was proved based on various quantitative evaluation indices.

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A bilevel optimization problem with deep learning based on fractional total variation for image denoising

Ben-loghfyry,

Hakim

2023

Multimed Tools Appl

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HINRDNet: A half instance normalization residual dense network for passive millimetre wave image restoration

Cited by 4 publications

References 37 publications

Time–Frequency Domain Seismic Signal Denoising Based on Generative Adversarial Networks

Time–Frequency Domain Seismic Signal Denoising Based on Generative Adversarial Networks

Performance Evaluation of L1-Norm-Based Blind Deconvolution after Noise Reduction with Non-Subsampled Contourlet Transform in Light Microscopy Images

A bilevel optimization problem with deep learning based on fractional total variation for image denoising

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