- 标题
- 摘要
- 关键词
- 实验方案
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Image Denoising Using Block-Rotation-Based SVD Filtering in Wavelet Domain
摘要： This paper proposes an image denoising method using singular value decomposition (SVD) with block-rotation-based operations in wavelet domain. First, we decompose a noisy image to some sub-blocks, and use the single-level discrete 2-D wavelet transform to decompose each sub-block into the low-frequency image part and the high-frequency parts. Then, we use SVD and rotation-based SVD with the rank-1 approximation to filter the noise of the different high-frequency parts, and get the denoised sub-blocks. Finally, we reconstruct the sub-block from the low-frequency part and the filtered the high-frequency parts by the inverse wavelet transform, and reorganize each denoised sub-blocks to obtain the final denoised image. Experiments show the effectiveness of this method, compared with relevant methods.
关键词： singular value decomposition,threshold denoising,structural similarity index,position,peak signal-to-noise ratio,image denoising
更新于2025-09-23 15:23:52
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Fringe pattern denoising based on deep learning
摘要： In this paper, deep learning as a novel algorithm is proposed to reduce the noise of the fringe patterns. Usually, the training samples are acquired through experimental acquisition, but these data can be easily obtained by simulations in the proposed algorithm. Thus, the time cost used for the whole training process is greatly reduced. The performance of the proposed algorithm has been demonstrated through the analysis on the simulated and real fringe patterns. It is obvious that the proposed algorithm has a faster calculation speed compared with existing denoising algorithm, and recovers the fringe patterns with high quality. Most importantly, the proposed algorithm may provide a solution to other denoising problems in the field of optics, such as hologram and speckle denoising.
关键词： Fringe pattern,Deep learning,Denoising
更新于2025-09-23 15:23:52
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Quaternion-based weighted nuclear norm minimization for color image denoising
摘要： The quaternion method plays an important role in color image processing, because it represents the color image as a whole rather than as a separate color space component, thus naturally handling the coupling among color channels. The weighted nuclear norm minimization (WNNM) scheme assigns different weights to different singular values, leading to more reasonable image representation method. In this paper, we propose a novel quaternion weighted nuclear norm minimization (QWNNM) model and algorithm under the low rank sparse framework. The proposed model represents the color image as a low rank quaternion matrix, where quaternion singular value decomposition can be calculated by its equivalent complex matrix. We solve the QWNNM by adaptively assigning different singular values with different weights. Color image denoising is implemented by QWNNM based on non-local similarity priors. In this new color space, the inherent color structure can be well preserved during image reconstruction. For high noise levels, we apply a Gaussian lowpass filter (LPF) to the noisy image as a preprocessing before QWNNM, which reduces the iteration numbers and improves the denoised results. The experimental results clearly show that the proposed method outperforms K-SVD, QKSVD and WNNM in terms of both quantitative criteria and visual perceptual.
关键词： Quaternion singular value decomposition,Non-local similarity priors,Quaternion weighted nuclear norm minimization,Low rank,Color image denoising
更新于2025-09-23 15:23:52
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Low-rank Bayesian tensor factorization for hyperspectral image denoising
摘要： In this paper, we present a low-rank Bayesian tensor factorization approach for hyperspectral image (HSI) denoising problem, where zero-mean white and homogeneous Gaussian additive noise is removed from a given HSI. The approach is based on two intrinsic properties underlying a HSI, i.e., the global correlation along spectrum (GCS) and nonlocal self-similarity across space (NSS). We first adaptively construct the patch-based tensor representation for the HSI to extract the NSS knowledge while preserving the property of GCS. Then, we employ the low rank property in this representation to design a hierarchical probabilistic model based on Bayesian tensor factorization to capture the inherent spatial-spectral correlation of HSI, which can be effectively solved under the variational Bayesian framework. Furthermore, through incorporating these two procedures in an iterative manner, we build an effective HSI denoising model to recover HSI from its corruption. This leads to a state-of-the-art denoising performance, consistently surpassing recently published leading HSI denoising methods in terms of both comprehensive quantitative assessments and subjective visual quality.
关键词： Hyperspectral image denoising,Global correlation along spectrum,Full Bayesian CP factorization,Nonlocal self-similarity,Variational Bayesian inference,Tensor rank auto determination
更新于2025-09-23 15:23:52
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Bidirectional Recurrent Auto-Encoder for Photoplethysmogram Denoising
摘要： Photoplethysmography (PPG) has become ubiquitous with the development of smartwatches and the mobile healthcare market. However, PPG is vulnerable to various types of noises which are ever-present in uncontrolled environments, and the key to obtaining meaningful signals depends on successful denoising of PPG. In this context, algorithms have been developed to denoise PPG, but many were validated in controlled settings or are reliant on multiple steps that must all work correctly. This paper proposes a novel PPG denoising algorithm based on bidirectional recurrent denoising auto-encoder (BRDAE) which requires minimal pre-processing steps and have the benefit of waveform feature accentuation beyond simple denoising. The BRDAE was trained and validated on a dataset with artificially augmented noise, and was tested on a large open-database of PPG signals collected from patients enrolled in intensive care units (ICUs) as well as from PPG data collected intermittently during the daily routine of 9 subjects over 24-hours. Denoising with the trained BRDAE improved signal-to-noise ratio of the noise-augmented data by 7.9dB during validation. In the test datasets, the denoised PPG showed statistically significant improvement in heart rate detection as compared to the original PPG in terms of correlation to reference and root-mean-squared error. These results indicate that the proposed method is an effective solution for denoising the PPG signal, and promises values beyond traditional denoising by providing PPG feature accentuation for pulse waveform analysis.
关键词： auto-encoder (AE),denoising,recurrent neural networks (RNN),photoplethysmography (PPG)
更新于2025-09-23 15:23:52
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Hyperspectral Mixed Denoising via Spectral Difference-Induced Total Variation and Low-Rank Approximation
摘要： Exploration of multiple priors on observed signals has been demonstrated to be one of the effective ways for recovering underlying signals. In this paper, a new spectral difference-induced total variation and low-rank approximation (termed SDTVLA) method is proposed for hyperspectral mixed denoising. Spectral difference transform, which projects data into spectral difference space (SDS), has been proven to be powerful at changing the structures of noises (especially for sparse noise with a specific pattern, e.g., stripes or dead lines present at the same position in a series of bands) in an original hyperspectral image (HSI), thus allowing low-rank techniques to get rid of mixed noises more efficiently without treating them as low-rank features. In addition, because the neighboring pixels are highly correlated and the spectra of homogeneous objects in a hyperspectral scene are always in the same low-dimensional manifold, we are inspired to combine total variation and the nuclear norm to simultaneously exploit the local piecewise smoothness and global low rankness in SDS for mixed noise reduction of HSI. Finally, the alternating direction methods of multipliers (ADMM) is employed to effectively solve the SDTVLA model. Extensive experiments on three simulated and two real HSI datasets demonstrate that, in terms of quantitative metrics (i.e., the mean peak signal-to-noise ratio (MPSNR), the mean structural similarity index (MSSIM) and the mean spectral angle (MSA)), the proposed SDTVLA method is, on average, 1.5 dB higher MPSNR values than the competitive methods as well as performing better in terms of visual effect.
关键词： ADMM,total variation,hyperspectral mixed denoising,low-rank approximation,spectral difference space
更新于2025-09-23 15:23:52
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[IEEE ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Calgary, AB (2018.4.15-2018.4.20)] 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Group Sparsity Residual with Non-Local Samples for Image Denoising
摘要： Inspired by group-based sparse coding, recently proposed group sparsity residual (GSR) scheme has demonstrated superior performance in image processing. However, one challenge in GSR is to estimate the residual by using a proper reference of the group-based sparse coding (GSC), which is desired to be as close to the truth as possible. Previous researches utilized the estimations from other algorithms (i.e., GMM or BM3D), which are either not accurate or too slow. In this paper, we propose to use the Non-Local Samples (NLS) as reference in the GSR regime for image denoising, thus termed GSR-NLS. More specifically, we first obtain a good estimation of the group sparse coefficients by the image non-local self-similarity, and then solve the GSR model by an effective iterative shrinkage algorithm. Experimental results demonstrate that the proposed GSR-NLS not only outperforms many state-of-the-art methods, but also delivers the competitive advantage of speed.
关键词： Image denoising,group sparsity residual,iterative shrinkage algorithm,group-based sparse coding,non-local self-similarity
更新于2025-09-23 15:23:52
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Denoising of low-dose CT images via low-rank tensor modeling and total variation regularization
摘要： Low-dose Computed Tomography (CT) imaging is a most commonly used medical imaging modality. Though the reduction in dosage reduces the risk due to radiation, it leads to an increase in noise level. Hence, it is a mandatory requirement to include a noise reduction technique as a pre-and/or post-processing step for better disease diagnosis. The nuclear norm minimization has attracted a great deal of research interest in contemporary years. This paper proposes a low-rank approximation based approach for denoising of CT images by effectively utilizing the global spatial correlation and local smoothness properties. The tensor nuclear norm is used to describe the global properties and the tensor total variation is used to characterize the local smoothness as well as to improve global smoothness. The resulting optimization problem is solved by the Alternative Direction Method of Multipliers (ADMM) technique. Experimental results on simulated and real CT data prove that the proposed methods outperform the state-of-art works.
关键词： Denoising,Computed tomography image,Tensor low rank recovery,Tensor total variation
更新于2025-09-23 15:23:52
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Spatiotemporal Adaptive Nonuniformity Correction Based on BTV Regularization
摘要： The residual nonuniformity response, ghosting artifacts, and over-smooth effects are the main defects of the existing nonuniformity correction (NUC) methods. In this paper, a spatiotemporal feature-based adaptive NUC algorithm with bilateral total variation (BTV) regularization is presented. The primary contributions of the innovative method are embodied in the following aspects: BTV regularizer is introduced to eliminate the nonuniformity response and suppress the ghosting effects. The spatiotemporal adaptive learning rate is presented to further accelerate convergence, remove ghosting artifacts, and avoid over-smooth. Moreover, the random projection-based bilateral filter is proposed to estimate the desired target image more accurately which yields more details in the actual scene. The experimental results validated that the proposed algorithm achieves outstanding performance upon both simulated data and real-world sequence.
关键词： infrared image sensors,Infrared imaging,neural networks,image denoising
更新于2025-09-23 15:23:52
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[IEEE 2018 International Conference on Communication and Signal Processing (ICCSP) - Chennai (2018.4.3-2018.4.5)] 2018 International Conference on Communication and Signal Processing (ICCSP) - A Comparative Analysis of Total Variation and Least Square Based Hyperspectral Image Denoising Methods
摘要： Hyperspectral image (HSI) with high spectral resolution will be always degraded by the noise accumulation. Therefore, image denoising is a fundamental preprocessing technique which improves the precision of successive processes like image classification, unmixing etc. In this paper, we compare least square (LS) weighted regularization in spectral domain with spatial least square and total variation (TV) denoising techniques. These methods are experimented on real, and noise simulated hyperspectral image datasets. The contrast and edges of the image are well preserved in the spectral LS. The image contrast varies in spatial LS, and edge informations are lost in TV. The experimental results show that, the spectral LS is superior to other two techniques in terms of visual interpretation, Signal-to-Noise Ratio (SNR) and Structural Similarity (SSIM) Index.
关键词： IBBC,SNR,Least Square,Hyperspectral Image,Denoising,Spectral domain,Total Variation,SSIM
更新于2025-09-23 15:22:29