Image Denoising Research Articles

Innovative design of wood texture images for indoor furniture based on variable space

In the design of furniture wood texture images, image restoration is a key issue. This study proposes a Bregmanized operator splitting optimization algorithm based on variable space. This study combines variable spatial morphology to process texture images and effectively extract image features using different operators, thereby achieving image restoration. The results of comparing the proposed algorithm with other image processing algorithms showed that the research algorithm achieved a peak signal-to-noise ratio of 29.86 and a structural similarity index of 0.87 in image denoising, respectively, and had a good denoising effect. In terms of image deblurring, the research algorithm had the lowest root mean square error values on the France and Boat datasets, with values of 8.98 and 8.82, respectively, indicating that the image processed by the algorithm had a high similarity with the real image. In terms of image resolution reconstruction, the peak signal-to-noise ratio and root mean square error values of the research algorithm reached 29.74 and 12.67, respectively, indicating that the reconstructed image had the best fit with the original image and the smallest error. In summary, the proposed algorithm has shown good performance in image processing and can be effectively applied in fields such as image denoising, deblurring, and resolution reconstruction. It provides effective methods and technical support for innovative design of wood texture images in indoor furniture.

A variable p[u] exponent reaction-diffusion PDE for image denoising

The paper presents a unique reaction-diffusion coupled system with p(u(t,x))-growth, employing the decomposition approach of H−1 norm. This method is designed to address image denoising by considering both texture and smooth components during the recovery process. The existence and uniqueness of weak solutions for the coupled system are established using Galerkin's method within a suitable space framework. Experimental analysis demonstrates the model's effectiveness in image restoration, and it is compared with other competitive models to showcase its performance.

Adaptive noise-aware denoising network: Effective denoising for CT images with varying noise intensity

X-ray computed tomography (CT) plays a crucial role in modern medical imaging for its non-invasive acquisition of anatomical information about the human body. However, its inherent carcinogenic risk remains an unavoidable topic. Low-dose CT (LDCT) reduces ionizing radiation exposure to minimize harm to the human body, but it introduces noise and artifacts of varying intensity, which can affect diagnosis and analysis. In recent years, deep learning has demonstrated competitive performance in medical image denoising. However, most current deep learning-based methods primarily focus on specific noise intensities, leading to degradation when faced with noise of different intensities. In this paper, we propose an Adaptive Noise-Aware Denoising Generative Adversarial Networks (ANAD-GAN) to address the aforementioned issues. The proposed noise-aware memory module can remember and decode high-level hidden features of noise of different intensities adaptively through self-updating feature vectors. To explore a more accurate perception of noise distribution, an intricately designed block named BLOGS is incorporated in our framework. We also integrate a discriminator-based detail-preserving loss in our framework to further enhance the visual quality of the denoised images. In summary, our innovative network dynamically adjusts to varying noise intensities, yielding remarkable denoising results. It attains a PSNR of 48.36 and SSIM of 0.9848 across 5936 images in the AAPM dataset, achieves a PSNR of 48.98 and SSIM of 0.9882 on the Philips dataset with 5000 images, and demonstrates outstanding performance with a PSNR of 50.74 and SSIM of 0.9906 on the private Siemens dataset comprising 4000 images. Through extensive experiments on three datasets, our method surpasses various widely adopted techniques in effectively addressing noise of different intensities.

Lipschitz energy functional for anisotropic diffusion applications

Image denoising remains a key research problem because of its potential role as a pre-processing component in image processing, computer vision, and machine learning tasks. Of the available approaches for image denoising, those inspired by anisotropic diffusion processes have been a center of discussion for decades. Despite the efforts and promising results achieved by diffusion-inspired denoising methods, we noted insufficient attention on the design of energy functionals for anisotropic diffusion equations. Most researchers consider heuristic approaches to design diffusivity functionals, a practice that cannot provide mathematical explanations on why their approaches work. The current research presents a strictly convex and Lipschitz energy functional that guarantees a unique solution for an evolutionary process. Based on this functional, we derive an anisotropic diffusion equation for image denoising applications. Experimental results show that an algorithm corresponding to the proposed equation is computationally efficient, and generates informative and visually appealing images with competitive values of peak signal-to-noise ratio and structural similarity. Guided by the compelling properties of our energy functional, we provide an additional insight to describe quality of the results. Implementation codes and test datasets of the proposed approach are publicly accessible at the MATLAB File Exchange (https://www.mathworks.com/matlabcentral/fileexchange/160108-lipschitz-diffusion-inspired-energy-functional).

Mini-infrared thermal imaging system image denoising with multi-head feature fusion and detail enhancement network

The mini-infrared thermal imaging system (MITIS) has been widely applied in outdoor photography, military observation, medical auxiliary diagnosis, and so on. However, due to the limitations of infrared sensor performance, imaging system size, and environment interference, the infrared images captured by the current MITIS generally encounter noise interference and low contrast issues. To solve these issues, we propose a multi-head feature fusion and detail enhancement network, namely MdNet, for complex features (texture and edges) retaining and denoising of infrared images obtained from the MITIS. Specifically, MdNet consists of a multi-head feature fusion block (MFFB), detail enhancement block (DEB), and reconstruction convolution. The MFFB is designed to extract and fuse multi-scale feature information, including multi-scale and feature fusion blocks. The DEB is developed to enhance the noise feature and reduce the loss of detailed information by three multi-level local feature attention blocks and residual learning. Quantitative and qualitative experimental results demonstrate that the MdNet achieves competitive performance in removing synthetic and natural noises compared with other state-of-the-art infrared image denoising methods and provides an effective solution for MITIS image denoising.

Zero-shot denoising of microscopy images recorded at high-resolution limits.

Conventional and electron microscopy visualize structures in the micrometer to nanometer range, and such visualizations contribute decisively to our understanding of biological processes. Due to different factors in recording processes, microscopy images are subject to noise. Especially at their respective resolution limits, a high degree of noise can negatively effect both image interpretation by experts and further automated processing. However, the deteriorating effects of strong noise can be alleviated to a large extend by image enhancement algorithms. Because of the inherent high noise, a requirement for such algorithms is their applicability directly to noisy images or, in the extreme case, to just a single noisy image without a priori noise level information (referred to as blind zero-shot setting). This work investigates blind zero-shot algorithms for microscopy image denoising. The strategies for denoising applied by the investigated approaches include: filtering methods, recent feed-forward neural networks which were amended to be trainable on noisy images, and recent probabilistic generative models. As datasets we consider transmission electron microscopy images including images of SARS-CoV-2 viruses and fluorescence microscopy images. A natural goal of denoising algorithms is to simultaneously reduce noise while preserving the original image features, e.g., the sharpness of structures. However, in practice, a tradeoff between both aspects often has to be found. Our performance evaluations, therefore, focus not only on noise removal but set noise removal in relation to a metric which is instructive about sharpness. For all considered approaches, we numerically investigate their performance, report their denoising/sharpness tradeoff on different images, and discuss future developments. We observe that, depending on the data, the different algorithms can provide significant advantages or disadvantages in terms of their noise removal vs. sharpness preservation capabilities, which may be very relevant for different virological applications, e.g., virological analysis or image segmentation.

Crack recognition and defect detection of assembly building constructions for intelligent construction

Vision-assisted surface defect detection technology is shallowly applied in crack identification of assembly building components, for this reason, the study proposes a crack identification and defect detection method for assembly building components oriented to intelligent construction. An image preprocessing algorithm is designed by improving bilateral filtering, on the basis of which an image classification model is constructed using the GhostNet algorithm, and the cracks are localized and measured using the 2D pixel positioning technique. Algorithm validation showed that the processed image denoising is better, and the peak signal-to-noise ratio of the image of the proposed algorithm is improved by 15.701 % and 2.395 %, respectively, compared to other algorithms. The F1 value of the proposed model after 50 training rounds increased by 20.970 % on average compared to other models, and the detection accuracy was as high as 0.990. The actual measurements of cracks in concrete wall panels revealed that the research-proposed method has better results compared to the traditional manual measurements, and is not subject to the limitations and interferences of factors such as manual experience, and it is more effective in the recognition of crack images. Overall, the detection method proposed by the study has high accuracy and small error, can meet the needs and standards of crack detection in assembly building components, and can intelligently locate the maximum length and width coordinates of the cracks, which is of high value in the application of crack detection in assembly building components.

FPGA-BASED IMPLEMENTATION OF A GAUSSIAN SMOOTHING FILTER WITH POWERS-OF-TWO COEFFICIENTS

The purpose of the study is to develop methods for synthesizing a Gaussian filter that ensures simplified hardware and software implementation, particularly filters with powers-of-two coefficients. Such filters can provide effective denoising of images, including landscape maps, both natural and synthetically generated. The study also involves analyzing of methods for FPGA implementation, comparing their hardware complexity, performance, and noise reduction with traditional Gaussian filters. Results. An algorithm for rounding filter coefficients to powers of two, providing optimal approximation of the constructed filter to the original, is presented, along with examples of developed filters. Topics covered include FPGA implementation, based on the Xilinx Artix-7 FPGA. Filter structures, testing methods, simulation results, and verification of the scheme are discussed. Examples of the technological placement of the implemented scheme on the FPGA chip are provided. Comparative evaluations of FPGA resources and performance for proposed and traditional Gaussian filters are carried out. Digital modeling of the filters and noise reduction estimates for noisy images of the terrain surface are presented. The developed algorithm provides approximation of Gaussian filter coefficients as powers of two for a given window size and maximum number of bits with a relative error of no more than 0.18. Implementing the proposed filters on FPGA results in a hardware costs reduction with comparable performance. Computer simulation show that Gaussian filters both traditional and proposed effectively suppress additive white noise in images. Proposed filters improve the signal-to-noise ratio within 5-10 dB and practically match the filtering quality of traditional Gaussian filters.

Multi-Branch Network for Color Image Denoising Using Dilated Convolution and Attention Mechanisms.

Image denoising is regarded as an ill-posed problem in computer vision tasks that removes additive noise from imaging sensors. Recently, several convolution neural network-based image-denoising methods have achieved remarkable advances. However, it is difficult for a simple denoising network to recover aesthetically pleasing images owing to the complexity of image content. Therefore, this study proposes a multi-branch network to improve the performance of the denoising method. First, the proposed network is designed based on a conventional autoencoder to learn multi-level contextual features from input images. Subsequently, we integrate two modules into the network, including the Pyramid Context Module (PCM) and the Residual Bottleneck Attention Module (RBAM), to extract salient information for the training process. More specifically, PCM is applied at the beginning of the network to enlarge the receptive field and successfully address the loss of global information using dilated convolution. Meanwhile, RBAM is inserted into the middle of the encoder and decoder to eliminate degraded features and reduce undesired artifacts. Finally, extensive experimental results prove the superiority of the proposed method over state-of-the-art deep-learning methods in terms of objective and subjective performances.

Novel Image Denoising Techniques Using AFMF

Background: In this paper, we have proposed a new image-denoising approach, which is a hybrid technique using the self-organizing migration algorithm (SOMA) and adaptive frequency median filter (AFMF). Materials and Methods: The first step in this approach consists of applying (AFMF) to the noisy image in order to have the first version of the denoised image. This first version of the denoised image is considered a clean image, which is then used as an input of an image-denoising system based on SOMA. This denoising system is then applied for denoising the noisy image and then a final version of the denoised image can be obtained. This image denoising system based on SOMA has two inputs, which are the noisy image and the corresponding clean image. However, we have available only the noisy image, and for that reason, we have first applied the AFMF to the noisy image and then obtained the first version of the denoised image as the clean image. In order to improve this proposed denoising technique, we have replaced the denoising system based on SOMA with targeted image denoising (TID), which is a more recent denoising approach. The PSNR (peak-SNR) and SSIM (structural similarity) have been used for evaluating the performance of the image-denoising techniques proposed in this work. Results: The results obtained from the computations of PSNR and SSIM show the performance of these proposed image-denoising techniques. Conclusion: The results obtained from the computations of PSNR and SSIM show that the proposed image-denoising techniques outperform a number of image-denoising approaches existing in the literature and used here for our evaluation.

Deep learning adversarial attacks and defenses on license plate recognition system

The breakthroughs in Machine learning and deep neural networks have revolutionized the handling of critical practical challenges, achieving state-of-the-art performance in various computer vision tasks. Notably, the application of deep neural networks in optical character recognition (OCR) has significantly enhanced the performance of OCR systems, making them a pivotal preprocessing component in text analysis pipelines for crucial applications such as license plate recognition (LPR) systems, where the efficiency of OCR is paramount. However, despite the advancements, the integration of deep neural networks in OCR introduces inherent security vulnerabilities, particularly susceptibility to adversarial examples. Adversarial examples in LPR systems are crafted by introducing perturbations to original license plate images, which can effectively compromise the integrity of the license plate recognition process, leading to erroneous license plate number identification. Given that the primary goal of OCR in this context is to accurately recognize license plate numbers, even a single misinterpreted character can significantly impact the overall performance of the LPR system. The vulnerability of LPR systems to adversarial attacks underscores the urgent need to address the security weaknesses inherited from deep neural networks. In response to these challenges, the exploration of alternative defense mechanisms, such as image denoising and in-painting, presents a compelling approach to bolstering the resilience of LPR systems against adversarial attacks. By prioritizing practical implementation and integration of image denoising and inpainting techniques align with the operational requirements of real-world LPR systems. These methods can be seamlessly integrated into existing pipelines, offering pragmatic and accessible means of enhancing security without imposing significant computational overhead. By embracing a multi-faceted approach that combines the strengths of traditional image processing techniques, the research endeavors to develop comprehensive and versatile defense strategies tailored to the specific vulnerabilities and requirements of LPR systems. This holistic approach aims to fortify LPR systems against adversarial threats, thereby fostering increased trust and reliability in the deployment of OCR and LPR technologies across various domains and applications.

Neural network denoising of x-ray images from high-energy-density experiments

Neural network denoising of x-ray images from high-energy-density experiments

An Edge Detection Method for Welding Pool Based on An Improved Canny Algorithm

Abstract In this study, an improved Canny algorithm is proposed for detecting welding pool edges in complex environments, considering the issues of inaccurate extraction, discontinuity, and susceptibility to noise in traditional edge detection algorithms. Firstly, the original Gaussian filter is replaced with a hybrid filter that combines dark channel prior defogging and bilateral filtering used for image denoising. Then, a four-direction Sobel operator is utilized to calculate gradient amplitude and direction. Furthermore, the double threshold required for extracting and connecting pool edges is obtained by using an enhanced quadratic Otsu algorithm. Experimental results demonstrate that, compared with the traditional algorithm, the improved Canny algorithm achieves an approximately 51.6% increase in PSNR and about 0.04 improvement in SSIM, enabling more accurate and comprehensive extraction of welding pool edge information.

Spach Transformer: Spatial and Channel-Wise Transformer Based on Local and Global Self-Attentions for PET Image Denoising.

Position emission tomography (PET) is widely used in clinics and research due to its quantitative merits and high sensitivity, but suffers from low signal-to-noise ratio (SNR). Recently convolutional neural networks (CNNs) have been widely used to improve PET image quality. Though successful and efficient in local feature extraction, CNN cannot capture long-range dependencies well due to its limited receptive field. Global multi-head self-attention (MSA) is a popular approach to capture long-range information. However, the calculation of global MSA for 3D images has high computational costs. In this work, we proposed an efficient spatial and channel-wise encoder-decoder transformer, Spach Transformer, that can leverage spatial and channel information based on local and global MSAs. Experiments based on datasets of different PET tracers, i.e., 18F-FDG, 18F-ACBC, 18F-DCFPyL, and 68Ga-DOTATATE, were conducted to evaluate the proposed framework. Quantitative results show that the proposed Spach Transformer framework outperforms state-of-the-art deep learning architectures.

Non-Local and Fully Connected Tensor Network Decomposition for Remote Sensing Image Denoising

Non-Local and Fully Connected Tensor Network Decomposition for Remote Sensing Image Denoising

Guided regularization and its application for image restoration

Variational regularization, renowned for its sound theoretical foundations and impressive performance, is widely used in image restoration. The traditional regularization models typically use a predefined regularizer to promote smoothness in the solution. However, these models do not explicitly take into account any external information that should be preserved in the restoration. In this paper, we introduce a novel guided regularization model to enhance the efficacy of traditional regularization. Our model incorporates an external guidance regularizer, utilizing a guidance image to bolster the quality of restoration. By integrating this external information into the regularization process, the model is better equipped to preserve specific features or attributes indicated by the guidance image, leading to more accurate and aesthetically pleasing restored images. Furthermore, we demonstrate the convexity of the model and prove the existence and uniqueness of the solution. The alternating direction method of multipliers (ADMM) algorithm is employed to numerically solve the proposed model. In the experimental evaluation, the proposed model is applied to image denoising and deblurring tasks. The experiments successfully validate the proposed model and algorithm. Compared with several state-of-the-art models, the proposed model demonstrates the best performance in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM).

A Dual-Branch Self-Boosting Network Based on Noise2Noise for Unsupervised Image Denoising

While unsupervised denoising models have shown progress in recent years, their noise reduction capabilities still lag behind those of supervised denoising models. This limitation can be attributed to the lack of effective constraints during training, which only utilizes noisy images and hinders further performance improvements In this work, we propose a novel dual-branch self-boosting network called DBSNet, which offers a straightforward and effective approach to image denoising. By leveraging task-dependent features, we exploit the intrinsic relationships between the two branches to enhance the effectiveness of our proposed model. Initially, we extend the classic Noise2Noise (N2N) architecture by adding a new branch for noise component prediction to the existing single-branch network designed for content prediction. This expansion creates a dual-branch structure, enabling us to simultaneously decompose a given noisy image into its content (clean) and noise components. This enhancement allows us to establish stronger constraint conditions and construct more powerful loss functions to guide the training process. Furthermore, we replace the UNet structure in the N2N network with the proven DnCNN (Denoising Convolutional Neural Network) sequential network architecture, which enhances the nonlinear mapping capabilities of the DBSNet. This modification enables our dual-branch network to effectively map a noisy image to its content (clean) and noise components simultaneously. To further improve the stability and effectiveness of training, and consequently enhance the denoising performance, we introduce a feedback mechanism where the network’s outputs, i.e., content and noise components, are fed back into the dual-branch network. This results in an enhanced loss function that ensures our model possesses excellent decomposition ability and further boosts the denoising performance. Extensive experiments conducted on both synthetic and real-world images demonstrate that the proposed DBSNet outperforms the unsupervised N2N denoising model as well as mainstream supervised models trained with supervised methods. Moreover, the evaluation results on real-world noisy images highlight the desirable generalization ability of DBSNet for practical denoising applications.

Improved Denoising of Cryo-Electron Microscopy Micrographs with Simulation-Aware Pretraining.

Cryo-electron microscopy (cryo-EM) has emerged as a potent technique for determining the structure and functionality of biological macromolecules. However, limited by the physical imaging conditions, such as low electron beam dose, micrographs in cryo-EM typically contend with an extremely low signal-to-noise ratio (SNR), impeding the efficiency and efficacy of subsequent analyses. Therefore, there is a growing demand for an efficient denoising algorithm designed for cryo-EM micrographs, aiming to enhance the quality of macromolecular analysis. However, owing to the absence of a comprehensive and well-defined dataset with ground truth images, supervised image denoising methods exhibit limited generalization when applied to experimental micrographs. To tackle this challenge, we introduce a simulation-aware image denoising (SaID) pretrained model designed to enhance the SNR of cryo-EM micrographs where the training is solely based on an accurately simulated dataset. First, we propose a parameter calibration algorithm for simulated dataset generation, aiming to align simulation parameters with those of experimental micrographs. Second, leveraging the accurately simulated dataset, we propose to train a deep general denoising model that can well generalize to real experimental cryo-EM micrographs. Comprehensive experimental results demonstrate that our pretrained denoising model achieves excellent denoising performance on experimental cryo-EM micrographs, significantly streamlining downstream analysis.

A review of deep learning and Generative Adversarial Networks applications in medical image analysis

Nowadays, computer-aided decision support systems (CADs) for the analysis of images have been a perennial technique in the medical imaging field. In CADs, deep learning algorithms are widely used to perform tasks like classification, identification of patterns, detection, etc. Deep learning models learn feature representations from images rather than handcrafted features. Hence, deep learning models are quickly becoming the state-of-the-art method to achieve good performances in different computer-aided decision-support systems in medical applications. Similarly, deep learning-based generative models called Generative Adversarial Networks (GANs) have recently been developed as a novel method to produce realistic-looking synthetic data. GANs are used in different domains, including medical imaging generation. The common problems, like class imbalance and a small dataset, in healthcare are well addressed by GANs, and it is a leading area of research. Segmentation, reconstruction, detection, denoising, registration, etc. are the important applications of GANs. So in this work, the successes of deep learning methods in segmentation, classification, cell structure and fracture detection, computer-aided identification, and GANs in synthetic medical image generation, segmentation, reconstruction, detection, denoising, and registration in recent times are reviewed. Lately, the review article concludes by raising research directions for DL models and GANs in medical applications.

Image Denoising using Wavelet Transformer

As digital imaging becomes increasingly important in various fields, the demand for effective methods to reduce image noise has risen. This study explores a wide range of techniques for denoising images, including both traditional and modern methods. It examines classical filters, statistical methods, and contemporary machine learning algorithms, explaining their principles, strengths, and weaknesses. Through a systematic review of existing literature, these techniques are categorized based on their underlying approaches and practical uses. Comparative analyses offer insights into the advantages and drawbacks of each method. Additionally, the paper discusses current trends and future directions in image denoising research. This comprehensive study serves as a valuable resource for researchers, professionals, and enthusiasts seeking a deep understanding of the evolving field of image denoising.