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Computed Tomography (CT) offers great visualization of the intricate internal body structures. To protect a patient from the potential radiation-related health risks, the acquisition of CT images should adhere to the “as low as reasonably allowed” (ALARA) standard. However, the acquired Low-dose CT (LDCT) images are inadvertently corrupted by artifacts and noise during the processes of acquisition, storage, and transmission, degrading the visual quality of the image and also causing the loss of image features and relevant information. Most recently, generative adversarial network (GAN) models based on deep learning (DL) have demonstrated ground-breaking performance to minimize image noise while maintaining high image quality. These models’ ability to adapt to uncertain noise distributions and representation-learning ability makes them highly desirable for the denoising of CT images. The state-of-the-art GANs used for LDCT image denoising have been comprehensively reviewed in this research paper. The aim of this paper is to highlight the potential of DL-based GAN for CT dose optimization and present future scope of research in the domain of LDCT image denoising.

Image denoising helps to strengthen the image statistics and the image processing scenario. Because of the inherent physical difficulties of various recording technologies, images are prone to the emergence of some noise during image acquisition. In the existing methods, poor illumination and atmospheric conditions affect the overall performance. To solve these issues, in this paper Political Taylor-Anti Coronavirus Optimization (Political Taylor-ACVO) algorithm is developed by integrating the features of Political Optimizer (PO) with Taylor series and Anti Coronavirus Optimization (ACVO). The input medical image is subjected to noisy pixel identification step, in which the deep residual network (DRN) is used to discover noise values and then pixel restoration process is performed by the created Political Taylor-ACVO algorithm. Thereafter image enhancement mechanism strategy is done using vectorial total variation (VTV) norm. On the other hand, original image is applied to discrete wavelet transform (DWT) such that transformed result is fed to non-local means (NLM) filter. An inverse discrete wavelet transform (IDWT) is utilized to the filtered outcome for generating the denoised image. Finally, image enhancement result is fused with denoised image computed through filtering model to compute fused output image. The proposed model observed the value for Peak signal-to-noise ratio (PSNR) of 29.167 dB, Second Derivative like Measure of Enhancement (SDME) of 41.02 dB, and Structural Similarity Index (SSIM) of 0.880 for Gaussian noise.

Face recognition in surveillance video depends on the surveillance camera and the surrounding environment. The problem becomes more complex to identify human faces in such surveillance videos when the frames have low resolution details and are affected by various noises. While several researchers have attempted to remove noise from degraded video frame images, there remains a void in the research of denoising surveillance images. It is observed that the existing denoising algorithms are not giving satisfactory results. Hence, we propose a Multistage Multifilter (MSMF) algorithm with four stages which employs super pixel segmentation followed by three different denoising techniques, namely Principal Component Analysis (PCA), Median, and anisotropic diffusion filtering, to denoise the low-resolution and noisy surveillance images. Overall, this approach to denoise surveillance images involves a combination of statistical, nonlinear, and edge-preserving filtering techniques to address the challenges posed by the noise present in these low-resolution images. The results are quite promising with 59.12% as the average Peak Signal to Noise Ratio (PSNR) value for Gaussian Noise and 60.15% as the average PSNR value for Salt and Pepper Noise.

Image denoising is essential for medical image analysis due to noise introduced by various acquisition methods and efforts to reduce radiation exposure. Noise in medical imaging from equipment, patient variability and environmental factors requires effective denoising to improve image quality and diagnostics. To address these challenges, a Multilevel Convolutional Neural Network with an optimized Visual Attention Network (MCVAN) is developed specifically for image denoising to enhance the Peak Signal-to-Noise Ratio (PSNR). Leopard Seal Optimization (LSO) is fine-tuning the parameters of the network, enhancing denoising performance. The motivation is to address the critical need for effective image denoising in medical imaging. The innovation of this research lies in the development of a MCVAN, developed for image denoising. The LSO to fine-tune parameters further enhances the denoising performance. This architecture effectively adapts to varying noise levels in input images, aiming to significantly reduce noise in medical images for improved diagnostic accuracy and visual clarity. Experimental results show an average PSNR of 43.79dB and a Structural Similarity Index Measure (SSIM) of 0.863 and the MCVAN achieves accuracy (99.9%), precision (99.9%), recall (99.9%) and $F$1-score (99.9%). Overall, the MCVAN demonstrates superior effectiveness in image denoising, surpassing existing techniques in both quality and efficiency.

The medical images are considered an essential part of healthcare applications. The noise associated with the images can minimize the clearness of the image, which leads to the misidentification of the diseases. Hence, the denoising of images is an essential process in healthcare applications. In this work, a medical image denoising method based on chronological walrus behavior optimization (CWaOA) is proposed. The process begins by identifying the noisy pixels in the image, which is achieved using LeNet to detect the noisy pixel map. The CWaOA algorithm is used for removing the noise from the image. To enhance the image pixels, the vectorial total variation (VTV) norm is employed. Simultaneously, the input image undergoes transformation using the dual-tree complex wavelet transform (DTCWT) to process its low-frequency components. A Gaussian filter is applied for image filtering, and the denoised image is obtained by applying inverse DTCWT. Based on the image quality accessed metrics like natural image quality evaluator (NIQE), universal quality index (UQI), and structural similarity index (SSIM), the denoised image and pixel-enhanced image fusion is done at the final step. Additionally, the performance of the model is evaluated using the peak signal-to-noise ratio (PSNR), second derivative-like measure of enhancement (SDME), and SSIM metrics, yielding superior results with PSNR of 30.03 dB, SDME of 41.94 dB, and SSIM of 0.883. The source code of the article is available at “ https://github.com/Rashmita-S/CWaOA.git”.

To remove image noise without considering the noise model, a dual-tree wavelet thresholding method (CDOA-DTDWT) is proposed through noise variance optimization. Instead of building a noise model, the proposed approach using the improved chaotic drosophila optimization algorithm (CDOA), to estimate the noise variance, and the estimated noise variance is utilized to modify wavelet coefficients in shrinkage function. To verify the optimization ability of the improved CDOA, the comparisons with basic DOA, GA, PSO and VCS are performed as well. The proposed method is tested to remove addictive noise and multiplicative noise, and denoising results are compared with other representative methods, e.g. Wiener filter, median filter, discrete wavelet transform-based thresholding (DWT), and nonoptimized dual-tree wavelet transform-based thresholding (DTDWT). Moreover, CDOA-DTDWT is applied as pre-processing utilization for tracking roller of mining machine as well. The experiment and application results prove the effectiveness and superiority of the proposed method.

Image denoising as a part of pre-processing in image analysis is a challenging area of research since noise removal and image detail preservation need a tradeoff. For classical denoising models, the convex total variation (TV) or some nonconvex regularizers are used to achieve the tradeoff. However, the denoising performance of classical models is still inadequate. To overcome this problem, this paper proposes a new variational model for image restoration, where a weighted regularizer is designed to protect more geometric structural details of images from over-smoothing and to remove much noise simultaneously. To solve the model efficiently, a novel algorithm based on Chambolle’s dual projection method and the iteratively reweighting method is presented. Numerical results prove that the proposed denoising method can show better performance than the classical TV-based and the nonconvex regularizer-based denoising methods.

Convolutional neural networks (CNNs) are becoming increasingly popular for image denoising. U-Nets, a type of CNN architecture, have been shown to be effective for this task. However, the impact of shallow layers on deeper layers decreases as the depth of the network increases. To address this issue, the authors propose a new image denoising method called DGANDU-Net. DGANDU-Net combines the DeblurGAN design with a U-Net architecture. This combination allows DGANDU-Net to effectively remove noise from images while preserving fine details. The authors also propose the use of two loss functions, mean square error (MSE) and perceptual loss, to improve the performance of DGANDU-Net. MSE is used to learn and improve the extracted features, while perceptual loss is used to produce the final denoised image. The authors evaluate the performance of DGANDU-Net on a variety of noise levels and find that it outperforms other state-of-the-art denoising algorithms in terms of both visual quality and two evaluation indices, including peak signal-to-noise ratio (PSNR) and Structural Similarity Index Measure (SSIM). Specifically, for extremely noisy environments with a noise standard deviation of 75, DGANDU-Net achieves an average PSNR of 37.39dB in the test dataset. The authors conclude that DGANDU-Net is a promising new method for image denoising that has the potential to significantly improve the quality of medical images used for diagnosis and treatment.

This study aimed to investigate the effect of an image denoising algorithm based on weighted low-rank matrix restoration on thyroid nodule ultrasound images. A total of 1000 original ultrasound image data sets of thyroid nodules were selected as the study samples. The nodule segmentation data set of thyroid ultrasound region of interest (ROI) images was drawn and acquired. By introducing multiscale features and an attention mechanism to optimize the U-Net model, an ultrasound image segmentation model (F-U-Net) was constructed. The performance of the traditional U network model and full convolutional neural network model (FCN) was analyzed and compared by simulation experiments. The results showed that the dice coefficient, accuracy, and recall of the improved loss function in this study were significantly higher than those of the traditional cross entropy loss function and dice coefficient loss function, and the differences were statistically significant (P < 0.05). The Dice coefficient, accuracy, and recall of the F-U-net model were significantly higher than those of the traditional FCN model and U-net model (P < 0.05). The diagnostic sensitivity, specificity, accuracy, and positive predictive value of the F-U-net model for benign and malignant thyroid nodules were significantly higher than those of the FCN model and U-net model (P < 0.05). In summary, the proposed F-U network can effectively process the ultrasound images of thyroid nodules, improve the image quality, and help to improve the diagnostic effect of benign and malignant thyroid nodules. It provides a data reference for segmentation and reconstruction of benign and malignant ultrasound images of thyroid nodules.

In recent years, sparse representations of images have shown to be efficient approaches for image recovery. Following this idea, this paper investigates incorporating a dictionary learning approach into fractal image coding, which leads to a new model containing three terms: a patch-based sparse representation prior over a learned dictionary, a quadratic term measuring the closeness of the underlying image to a fractal image, and a data-fidelity term capturing the statistics of Gaussian noise. After the dictionary is learned, the resulting optimization problem with fractal coding can be solved effectively. The new method can not only efficiently recover noisy images, but also admirably achieve fractal image noiseless coding/compression. Experimental results suggest that in terms of visual quality, peak-signal-to-noise ratio, structural similarity index and mean absolute error, the proposed method significantly outperforms the state-of-the-art methods.

Fractal coding has been widely used as an image compression technique in many image processing problems in the past few decades. On the other hand side, most of the natural images have the characteristic of nonlocal self-similarity that motivates low-rank representations of them. We would employ both the fractal image coding and the nonlocal self-similarity priors to achieve image compression in image denoising problems. Specifically, we propose a new image denoising model consisting of three terms: a patch-based nonlocal low-rank prior, a data-fidelity term describing the closeness of the underlying image to the given noisy image, and a quadratic term measuring the closeness of the underlying image to a fractal image. Numerical results demonstrate the superior performance of the proposed model in terms of peak-signal-to-noise ratio, structural similarity index and mean absolute error.

Image denoising has been a fundamental problem in the field of image processing. In this paper, we tackle removing impulse noise by combining the fractal image coding and the nonlocal self-similarity priors to recover image. The model undergoes a two-stage process. In the first phase, the identification and labeling of pixels likely to be corrupted by salt-and-pepper noise are carried out. In the second phase, image denoising is performed by solving a constrained convex optimization problem that involves an objective functional composed of three terms: a data fidelity term to measure the similarity between the underlying and observed images, a regularization term to represent the low-rank property of a matrix formed by nonlocal patches of the underlying image, and a quadratic term to measure the closeness of the underlying image to a fractal image. To solve the resulting problem, a combination of proximity algorithms and the weighted singular value thresholding operator is utilized. The numerical results demonstrate an improvement in the structural similarity (SSIM) index and peak signal-to-noise ratio.

Recorded medical images often represent a degraded version of the original scene due to imperfections in electronic or photographic medium used. The degradations may have many causes, but two dominant degradations are noise and blur. Restoration of blurred and noisy medical images is of fundamental importance in several medical imaging applications. Most of the medical image denoising techniques need removal of blur before the denoising. Denoising of medical images in presence of blur is a hard problem. Most of the wavelet transform-based denoising techniques use the orthonormal wavelets and suitable for image corrupted with only additive white Gaussian noise. In the present work, we have proposed a denoising algorithm for medical images based on the lifting-scheme and linear phase characteristic of biorthogonal wavelet transform. A level-dependent soft thresholding function has been used which is based on the standard deviation, the absolute mean and the absolute median of the wavelet coefficients. The linear phase characteristic of the biorthogonal filters used in denoising reduces the distortions at edge points of image. Also, the lifting schemes of the biorthogonal wavelet filters make the algorithm efficient and applicable in real time. Experimental results show that the proposed denoising method outperform standard wavelet, complex wavelet and curvelet-based denoising techniques in terms of the SNR and PSNR (in dB) and it offers effective noise removal from noisy medical images while maintaining sharpness of objects in the image.

This paper presents an improved anisotropic diffusion model which is based on a new diffusion coefficient and fractional order differential for image denoising. In the proposed model, the new diffusion coefficient can protect edges and fine characteristics from being over-smoothed. The fractional order differential is applied to weaken the staircasing effect, preserve fine characteristics. Additionally, the automatic set method of diffusion coefficient threshold is developed. Comparative experiments show that the proposed model succeeds in denoising and preserving fine characteristics.

This paper presents a new diffusion coefficient which is based on second order derivative and local entropy information for image denoising. In the proposed model, a second order derivative term is introduced, which reduces the staircasing effect and preserves edge in a processed image. The local entropy information can preserve texture. The Perona–Malik model with a new diffusion coefficient improves the denoised effects, and prevents edges from being over-smoothed. Comparative experiments show that the proposed model obtains more satisfied results than the other two existing models.

Gaussian noise is an important problem in computer vision. The novel methods that become popular in recent years for Gaussian noise reduction are Bayesian techniques in wavelet domain. In wavelet domain, the Bayesian techniques require a prior distribution of wavelet coefficients. In general case, the wavelet coefficients might be better modeled by non-Gaussian density such as Laplacian, two-sided gamma, and Pearson type VII densities. However, statistical analysis of textural image is Gaussian model. So, we require flexible model between non-Gaussian and Gaussian models. Indeed, Gumbel density is a suitable model. So, we present new Bayesian estimator for Gumbel random vectors in AWGN (additive white Gaussian noise). The proposed method is applied to dual-tree complex wavelet transform (DT-CWT) as well as orthogonal discrete wavelet transform (DWT). The simulation results show that our proposed methods outperform the state-of-the-art methods qualitatively and quantitatively.

Images are widely used in engineering. Unfortunately, ultrasound images are mainly degraded by an intrinsic noise called speckle. Therefore, de-speckling is a critical preprocessing step. Therefore, a robust despeckling method and accurate evaluation of images are suggested. We suggest three phases and a three-step denoising filter. In the first phase, the coefficients of variation are computed from the noisy image. The second phase is a three-step denoising filter. The first step is denoising of extreme levels of homogeneous regions, based on fuzzy homogeneous regions. The second step is a proposed adaptive bilateral filter (ABF). The ABF helps for better denoising based on the three regions which are edge, detail and homogeneous regions. The next step, a weight, is applied to the ABF. This step is for isolated noise denoising. Next, in the third phase, the output image is evaluated by the fuzzy logic approach. The proposed method is compared with other filters in the literature. The experimental outcomes show that the proposed method has better performance than the other filters. That proposed denoising algorithm is able to preserve image details and edges when compared with other denoising methods.

Image classification is a complicated process of classifying an image based on its visual representation. This paper portrays the need for adapting and applying a suitable image enhancement and denoising technique in order to arrive at a successful classification of data captured remotely. Biometric properties that are widely explored today are very important for authentication purposes. Noise may be the result of incorrect vein detection in the accepted image, thus explaining the need for a better development technique. This work provides subjective and objective analysis of the performance of various image enhancement filters in the spatial domain. After performing these pre-processing steps, the vein map and the corresponding vein graph can be easily obtained with minimal extraction steps, in which the appropriate Graph Matching method can be used to evaluate hand vein graphs thus performing the person authentication. The analysis result shows that the image enhancement filter performs better as an image enhancement filter compared to all other filters. Image quality measures (IQMs) are also tabulated for the evaluation of image quality.

This study proposes an image denoising algorithm based on sparse representation and Principal Component Analysis (PCA). The proposed algorithm includes the following steps. First, the noisy image is divided into overlapped $8\times 8$ blocks. Second, the discrete cosine transform is applied as a dictionary for the sparse representation of the vectors created by the overlapped blocks. To calculate the sparse vector, the orthogonal matching pursuit algorithm is used. Then, the dictionary is updated by means of the PCA algorithm to achieve the sparsest representation of vectors. Since the signal energy, unlike the noise energy, is concentrated on a small dataset by transforming into the PCA domain, the signal and noise can be well distinguished. The proposed algorithm was implemented in a MATLAB environment and its performance was evaluated on some standard grayscale images under different levels of standard deviations of white Gaussian noise by means of peak signal-to-noise ratio, structural similarity indexes, and visual effects. The experimental results demonstrate that the proposed denoising algorithm achieves significant improvement compared to dual-tree complex discrete wavelet transform and K-singular value decomposition image denoising methods. It also obtains competitive results with the block-matching and 3D filtering method, which is the current state-of-the-art for image denoising.

Images are widely used in engineering. Unfortunately, medical ultrasound images and synthetic aperture radar (SAR) images are mainly degraded by an intrinsic noise called speckle. Therefore, de-speckling is a main pre-processing stage for degraded images. In this paper, first, an optimized adaptive Wiener filter (OAWF) is proposed. OAWF can be applied to the input image without the need for logarithmic transform. In addition its performance is improved. Next, the coefficient of variation (CV) is computed from the input image. With the help of CV, the guided filter converts to an improved guided filter (IGF). Next, the improved guided filter is applied on the image. Subsequently, the fast bilateral filter is applied on the image. The proposed filter has a better image detail preservation compared to some other standard methods. The experimental outcomes show that the proposed denoising algorithm is able to preserve image details and edges compared with other de-speckling methods.