- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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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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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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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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[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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Hyperspectral Image Denoising Based on Spectral Dictionary Learning and Sparse Coding
摘要： Processing and applications of hyperspectral images (HSI) are limited by the noise component. This paper establishes an HSI denoising algorithm by applying dictionary learning and sparse coding theory, which is extended into the spectral domain. First, the HSI noise model under additive noise assumption was studied. Considering the spectral information of HSI data, a novel dictionary learning method based on an online method is proposed to train the spectral dictionary for denoising. With the spatial–contextual information in the noisy HSI exploited as a priori knowledge, the total variation regularizer is introduced to perform the sparse coding. Finally, sparse reconstruction is implemented to produce the denoised HSI. The performance of the proposed approach is better than the existing algorithms. The experiments illustrate that the denoising result obtained by the proposed algorithm is at least 1 dB better than that of the comparison algorithms. The intrinsic details of both spatial and spectral structures can be preserved after significant denoising.
关键词： image processing,hyperspectral image,spectral dictionary,image denoising,sparse coding
更新于2025-09-23 15:22:29
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Research on Image Restoration Algorithms Based on BP Neural Network
摘要： With the development of information transmission technology and computer technology, information acquisition mode is mainly converted from character to image nowadays. However, in the process of acquiring and transmitting images, image damage and quality decrease due to various factors. Therefore, how to restore image has become a research hotspot in the field of image processing. This paper establishes an image restoration model based on BP neural network. The simulation results show that the proposed method has made a great improvement compared with the traditional image restoration method.
关键词： image processing,BP neural network,image restoration,image denoising
更新于2025-09-23 15:22:29
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An improved infrared image processing method based on adaptive threshold denoising
摘要： This paper combines the image adaptive threshold denoising algorithm and performs double threshold mapping processing to the infrared image, which effectively reduces the influence of these phenomena to the infrared image and improves the quality of the image. In this paper, the infrared image denoising technology is studied, and an infrared image denoising method based on the wavelet coefficient threshold processing is proposed. This method is based on the noise distribution characteristics of infrared images, the multiplicative noise in the infrared image is transformed into an additive noise, and the wavelet transform coefficient of the transformed infrared image is processed to denoise the image. On this basis, the advantages and disadvantages of the soft and hard threshold functions are deeply analyzed, and an adaptive threshold function with adjustable parameter is constructed. At the same time, in order to suppress the Gibbs visual distortion caused by the absence of translation invariance of the orthogonal wavelet transform, the two-input wavelet transform with translation invariance is introduced, and a double threshold mapping infrared image processing method based on the adaptive threshold denoising algorithm based on the two-input wavelet transform is formed. Simulation results show that the method proposed in this paper has a better suppression of noise, maintains the integrity of image details, and improves the image quality to a certain extent.
关键词： Threshold function,Double threshold mapping,Image denoising,Binary wavelet transform,Infrared image
更新于2025-09-23 15:22:29
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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
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Dual tree complex wavelet transform incorporating SVD and bilateral filter for image denoising
摘要： In recent years massive production of digital images increased the need for image denoising. The effect of noise can be removed by using spatial and frequency domain approaches. Discrete Wavelet Transforms (DWT) is a frequency domain approach, which removes the noise by shrinking the wavelet coefficients using simple threshold value. Even though wavelet transform is popularly used in image processing applications, shift variance and poor directional selectivity are the two noteworthy limitations. In order to overcome these limitations, Dual Tree Complex Wavelet Transform (DTCWT) is used for perfect reconstruction of noisy image. A DTCWT incorporating Singular Value Decomposition (SVD) with Frobenius energy correcting factor and bilateral filter for image denoising using bivariate shrinkage function for thresholding the image is proposed in this paper. The denoising performance of the proposed method in terms of PSNR and it indicates that the proposed method outperforms over other existing techniques.
关键词： bilateral filter,SVD,bivariate shrinkage,thresholding technique,wavelet transform,DTCWT,image denoising
更新于2025-09-23 15:22:29