Image Noise Removal Research Articles

Recognizing objects in complex scenes: A Recent Systematic Review

This systematic review provides a comprehensive examination of recent advancements in object recognition within complex scenes, focusing on addressing challenges such as clutter, occlusion, and diverse environmental conditions. Leveraging the PRISMA framework, the study meticulously analysed a diverse range of literature from esteemed sources, employing advanced search methods on Scopus and WoS databases to identify and analyse primary research studies (n = 25). The review encompasses three key themes: Theme 1 concentrates on Image Noise Identification and Removal Techniques, while Theme 2 delves into Image Classification and Recognition under Noise. Additionally, Theme 3 explores Innovative Models and Approaches for Noise-Robust Image Analysis. Despite the progress achieved, contemporary recognition systems struggle with real-world complexities such as varied scales, lighting variations, and different viewpoints. The synthesis of findings emphasizes the necessity for innovative strategies that capitalize on contextual cues and harness the potential of deep learning to enhance precision in object recognition within intricate visual environments. The insights gleaned from this synthesis are poised to guide future research directions, informing the development of more resilient algorithms capable of navigating challenges and catalysing advancements in the field of object recognition.

A low-dose CT image denoising method based on state space model

Abstract Low-dose CT medical imaging techniques introduce noise while reducing radiation risks, necessitating denoising processing. However, existing mainstream denoising methods face a difficult trade-off between preserving image detail information and accurately removing noise. To address this issue, we propose a low-dose CT image denoising method based on a state space model. Firstly, a dynamic edge information enhancement module is introduced to automatically extract edge information from images using a learnable LoG operator and fuse it into feature layers at different scales to suppress edge information loss caused by denoising processes. Secondly, a U-net encoder based on state space estimation is designed to dynamically model spatial relationships between pixels through neighborhood filtering, enabling consideration of local differences in pixel values during denoising and better preservation of edges and textures. Compared to existing denoising methods, our approach achieves stable noise removal in low-dose CT images while preserving the original texture structure.

Column fixed-pattern noise removal in solar images using two-way filtering

Column fixed-pattern noise removal in solar images using two-way filtering

Retinex Image Enhancement Based on Sequential Decomposition With a Plug-and-Play Framework.

The Retinex model is one of the most representative and effective methods for low-light image enhancement. However, the Retinex model does not explicitly tackle the noise problem and shows unsatisfactory enhancing results. In recent years, due to the excellent performance, deep learning models have been widely used in low-light image enhancement. However, these methods have two limitations. First, the desirable performance can only be achieved by deep learning when a large number of labeled data are available. However, it is not easy to curate massive low-/normal-light paired data. Second, deep learning is notoriously a black-box model. It is difficult to explain their inner working mechanism and understand their behaviors. In this article, using a sequential Retinex decomposition strategy, we design a plug-and-play framework based on the Retinex theory for simultaneous image enhancement and noise removal. Meanwhile, we develop a convolutional neural network-based (CNN-based) denoiser into our proposed plug-and-play framework to generate a reflectance component. The final image is enhanced by integrating the illumination and reflectance with gamma correction. The proposed plug-and-play framework can facilitate both post hoc and ad hoc interpretability. Extensive experiments on different datasets demonstrate that our framework outcompetes the state-of-the-art methods in both image enhancement and denoising.

Iterative decoupling deconvolution network for image restoration

The iterative decoupled deblurring BM3D (IDDBM3D) (Danielyan et al., 2011) combines the analysis representation and the synthesis representation, where deblurring and denoising operations are decoupled, so that both problems can be easily solved. However, the IDDBM3D has some limitations. First, the analysis transformation and the synthesis transformation are analytical, thus have limited representation ability. Second, it is difficult to effectively remove image noise from threshold transformation. Third, there exists hyper-parameters to be tuned manually, which is difficult and time consuming. In this work, we propose an iterative decoupling deconvolution network(IDDNet), by unrolling the iterative decoupling algorithm of the IDDBM3D. In the proposed IDDNet, the analysis/synthesis transformation are implemented by encoder/decoder modules; the denoising is implemented by convolutional neural network based denoiser; the hyper-parameters are estimated by hyper-parameter module. We apply our models for image deblurring and super-resolution. Experimental results show that the IDDNet significantly outperforms the state-of-the-art unfolding networks.

AsymUNet: An Efficient Multi-Layer Perceptron Model Based on Asymmetric U-Net for Medical Image Noise Removal

With the continuous advancement of deep learning technology, U-Net–based algorithms for image denoising play a crucial role in medical image processing. However, most U-Net-based medical image denoising algorithms typically have large parameter sizes, which poses significant limitations in practical applications where computational resources are limited or large-scale patient data processing are required. In this paper, we propose a medical image denoising algorithm called AsymUNet, developed using an asymmetric U-Net framework and a spatially rearranged multilayer perceptron (MLP). AsymUNet utilizes an asymmetric U-Net to reduce the computational burden, while a multiscale feature fusion module enhances the feature interaction between the encoder and decoder. To better preserve the image details, spatially rearranged MLP blocks serve as the core building blocks of AsymUNet. These blocks effectively extract both the local and global features of the image, reducing the model’s reliance on prior knowledge of the image and further accelerating the training and inference processes. Experimental results demonstrate that AsymUNet achieves superior performance metrics and visual results compared with other state-of-the-art methods.

An improved attentive residue multi-dilated network for thermal noise removal in magnetic resonance images

Magnetic resonance imaging (MRI) technology is crucial in the medical field, but the thermal noise in the reconstructed MR images may interfere with the clinical diagnosis. Removing the thermal noise in MR images mainly contains two challenges. First, thermal noise in an MR image obeys Rician distribution, where the statistical features are not consistent in different regions of the image. In this case, conventional denoising methods like spatial convolutional filtering will not be appropriate to deal with it. Second, details and edge information in the image may get damaged while smoothing the noise. This paper proposes a novel deep-learning model to denoise MR images. First, the model learns a binary mask to separate the background and signal regions of the noised image, making the noise left in the signal region obey a unified statistical distribution. Second, the model is designed as an attentive residual multi-dilated network (ARM-Net), composed of a multi-branch structure, and supplemented with a frequency-domain-optimizable discrete cosine transform module. In this way, the deep-learning model will be more effective in removing the noise while maintaining the details of the original image. Furthermore, we have also made improvements on the original ARM-Net baseline to establish a new model called ARM-Net v2, which is more efficient and effective. Experimental results illustrate that over the BraTS 2018 dataset, our method achieves the PSNR of 39.7087 and 32.6005 at noise levels of 5% and 20%, which realizes the state-of-the-art performance among existing MR image denoising methods.

A Review on FPGA-based Architectures for Noise Removal of Digital Images using High-Level Design

Image corruption during the acquisition and transmission operations may be caused by a variety of disturbances. Scientists are still having trouble getting rid of the noise in the original picture. picture denoising is a technique used to improve a picture's visual quality and extract small details from a damaged image. Improving an image to make it crisper than it was originally is the aim of image restoration. The field of image processing is vast. This paper presents the results of some significant work in the subject of image denoising. It looks into image denoising using some of the most recent methods, such as SEPD (Simple Edge Preserved De-noising) and RSEPD (Reduced Simple Edge Preserved De- noising).Image corruption during the acquisition and transmission operations may be caused by a variety of disturbances. Scientists are still having trouble getting rid of the noise in the original picture. picture denoising is a technique used to improve a picture's visual quality and extract small details from a damaged image. Improving an image to make it crisper than it was originally is the aim of image restoration. The field of image processing is vast. This paper presents the results of some significant work in the subject of image denoising. It looks into image denoising using some of the most recent methods, such as SEPD (Simple Edge Preserved De-noising) and RSEPD (Reduced Simple Edge Preserved De- noising). Keywords— Power consumption analysis, Digital logic circuits, Ultra-low power, Subthreshold operation, Static CMOS gates

Continuum topological derivative - a novel application tool for denoising CT and MRI medical images

BackgroundCT and MRI modalities are important diagnostics tools for exploring the anatomical and tissue properties, respectively of the human beings. Several advancements like HRCT, FLAIR and Propeller have advantages in diagnosing the diseases very accurately, but still have enough space for improvements due to the presence of inherent and instrument noises. In the case of CT and MRI, the quantum mottle and the Gaussian and Rayleigh noises, respectively are still present in their advanced modalities of imaging. This paper addresses the denoising problem with continuum topological derivative technique and proved its trustworthiness based on the comparative study with other traditional filtration methods such as spatial, adaptive, frequency and transformation techniques using measures like visual inspection and performance metrics.MethodsThis research study focuses on identifying a novel method for denoising by testing different filters on HRCT (High-Resolution Computed Tomography) and MR (Magnetic Resonance) images. The images were acquired from the Image Art Radiological Scan Centre using the SOMATOM CT and SIGNA Explorer (operating at 1.5 Tesla) machines. To compare the performance of the proposed CTD (Continuum Topological Derivative) method, various filters were tested on both HRCT and MR images. The filters tested for comparison were Gaussian (2D convolution operator), Wiener (deconvolution operator), Laplacian and Laplacian diagonal (2nd order partial differential operator), Average, Minimum, and Median (ordinary spatial operators), PMAD (Anisotropic diffusion operator), Kuan (statistical operator), Frost (exponential convolution operator), and HAAR Wavelet (time–frequency operator). The purpose of the study was to evaluate the effectiveness of the CTD method in removing noise compared to the other filters. The performance metrics were analyzed to assess the diligence of noise removal achieved by the CTD method. The primary outcome of the study was the removal of quantum mottle noise in HRCT images, while the secondary outcome focused on removing Gaussian (foreground) and Rayleigh (background) noise in MR images. The study aimed to observe the dynamics of noise removal by examining the values of the performance metrics.In summary, this study aimed to assess the denoising ability of various filters in HRCT and MR images, with the CTD method being the proposed approach. The study evaluated the performance of each filter using specific metrics and compared the results to determine the effectiveness of the CTD method in removing noise from the images.ResultsBased on the calculated performance metric values, it has been observed that the CTD method successfully removed quantum mottle noise in HRCT images and Gaussian as well as Rayleigh noise in MRI. This can be evidenced by the PSNR (Peak Signal-to-Noise Ratio) metric, which consistently exhibited values ranging from 50 to 65 for all the tested images. Additionally, the CTD method demonstrated remarkably low residual values, typically on the order of e−09, which is a distinctive characteristic across all the images. Furthermore, the performance metrics of the CTD method consistently outperformed those of the other tested methods. Consequently, the results of this study have significant implications for the quality, structural similarity, and contrast of HRCT and MR images, enabling clinicians to obtain finer details for diagnostic purposes.ConclusionContinuum topological derivative algorithm is found to be constructive in removing prominent noises in both CT and MRI images and can serve as a potential tool for recognition of anatomical details in case of diseased and normal ones. The results obtained from this research work are highly inspiring and offer great promise in obtaining accurate diagnostic information for critical cases such as Thoracic Cavity Carina, Brain SPI Globe Lens 4th Ventricle, Brain-Middle Cerebral Artery, Brain-Middle Cerebral Artery and neoplastic lesions. These findings lay the foundation for implementing the proposed CTD technique in routine clinical diagnosis.

An Underwater Image Denoising Method Based on High-Frequency Abrupt Signal Separation and Hybrid Attention Mechanism.

During underwater image processing, image quality is affected by the absorption and scattering of light in water, thus causing problems such as blurring and noise. As a result, poor image quality is unavoidable. To achieve overall satisfying research results, underwater image denoising is vital. This paper presents an underwater image denoising method, named HHDNet, designed to address noise issues arising from environmental interference and technical limitations during underwater robot photography. The method leverages a dual-branch network architecture to handle both high and low frequencies, incorporating a hybrid attention module specifically designed for the removal of high-frequency abrupt noise in underwater images. Input images are decomposed into high-frequency and low-frequency components using a Gaussian kernel. For the high-frequency part, a Global Context Extractor (GCE) module with a hybrid attention mechanism focuses on removing high-frequency abrupt signals by capturing local details and global dependencies simultaneously. For the low-frequency part, efficient residual convolutional units are used in consideration of less noise information. Experimental results demonstrate that HHDNet effectively achieves underwater image denoising tasks, surpassing other existing methods not only in denoising effectiveness but also in maintaining computational efficiency, and thus HHDNet provides more flexibility in underwater image noise removal.

Progressive Feature Fusion Attention Dense Network for Speckle Noise Removal in OCT Images.

Although deep learning for Big Data analytics has achieved promising results in the field of optical coherence tomography (OCT) image denoising, the low recognition rate caused by complex noise distribution and a large number of redundant features is still a challenge faced by deep learning-based denoising methods. Moreover, the network with large depth will bring high computational complexity. To this end, we propose a progressive feature fusion attention dense network (PFFADN) for speckle noise removal in OCT images. We arrange densely connected dense blocks in the deep convolution network, and sequentially connect the shallow convolution feature map with the deep one extracted from each dense block to form a residual block. We add attention mechanism to the network to extract the key features and suppress the irrelevant ones. We fuse the output feature maps from all dense blocks and input them to the reconstruction output layer. We compare PFFADN with the state-of-the-art denoising algorithms on retinal OCT images. Experiments show that our method has better improvement in denoising performance.

An edge preserving median filter for images based on level-sets

We propose an edge preserving median filter, called the level-set adaptive median filter, for noise removal in images. This filter uses connected sets of pixels with the same value, namely level-sets, as flexible regions which contour to edges in the image. The filter determines whether a set is noise or signal and smooths the noise. These set regions are flexible in terms of shape since they are created based on their values, and being data-driven therefore provide the mechanism for the filter to preserve edges in the image. We used metrics such as Pratt's Figure of Merit and Peak-Signal-to-Noise Ratio on the labelled faces in the wild data set. We concluded that the proposed level-set adaptive median filter does remove noise while preserving the edges in the image better than the traditional adaptive median filter.

DAF‐Retinex: Preserve the image detailed features and restore the reflected image

AbstractCurrently, deep learning methods for low‐light image enhancement tasks mainly focus on the illumination of images, while neglecting the problems of image noise and feature loss. To address this issue, this paper proposes a novel low‐light image enhancement network called DAF‐Retinex, based on the Retinex‐Net. To address the issue of image noise, different from traditional image denoise methods, this paper utilizes a fully convolutional neural network to denoise the reflection component, additionally, a denoising loss function is introduced to suppress noise. For preserving image details and extracting features, this paper creatively introduces self‐calibrated convolutions into low‐light image enhancement tasks, furthermore, a feature augmented attention block consisting of feature‐guided attention (FGA) is designed for feature learning to effectively enhance image illumination and extract image detail features. Experimental results demonstrate that the proposed algorithm in this paper effectively removes image noise and extracts detailed features, resulting in visually improved outcomes. On public datasets, the average improvement in objective evaluation metrics of image quality such as PSNR, SSIM, and NIQE are 1.13%, 4.12%, and 1.28%, respectively.

Retraction Note: Adaptive hexagonal fuzzy hybrid filter for Rician noise removal in MRI images

Retraction Note: Adaptive hexagonal fuzzy hybrid filter for Rician noise removal in MRI images

Nonlinear parabolic double phase variable exponent systems with applications in image noise removal

In this paper, a novel parabolic system involving nonlinear and nonhomogeneous partial differential operator with variable growth structure is introduced for investigating the image denoising and restoration. More precisely, our model is based on regularizing the classical models involving variable exponent operators by considering a nonlinear operator with double phase flux. We begin by investigating theoretically the solvability to the parabolic system under consideration. Under the setting of Musielak-Orlicz spaces, we build a suitable functional framework to study the proposed system. Therefore, we develop the Faedo-Galerkin approach to demonstrate the existence and uniqueness of a weak solution to our model. To illustrate our theoretical results in the context of image noise removal, we present various numerical implementations on some grayscale images. To enrich these simulations, we test the robustness efficiency of the proposed model in the so-called Magnetic Resonance Images (MRI). The obtained numerical results claim that our model is more efficient and robust against noise, in comparison (visually and quantitatively) to some existing state-of-the-art methods.

Research on Removing Image Noise and Distortion in Machine Dial Recognition

This study aims to address the issues of image noise and distortion in machine dial recognition via initial denoising. A wavelet local threshold denoising method that combines high-frequency wavelet coefficients with wavelet decomposition coefficients in various directions is proposed. This method shows good results on 102 images of car dashboards, aircraft instrument panels, spacecraft displays, and dial instruments on robots. Although a few denoised images exhibit distortion due to intense lighting or heavy contamination, the denoising accuracy for the remaining images is 98.04%, demonstrating substantial practical value. Future research will concentrate on addressing complex image noise and structures.

Anisotropic equation based on fractional diffusion tensor for image noise removal

In this paper, we propose an anisotropic diffusion equation based on fractional‐order tensor diffusion applied on noisy images. We start by investigating the existence and uniqueness results of the proposed model. We also provide a discretization scheme based on the finite difference method. In order to prove the efficacy of our proposed approach, we deliver some simulations, which show robustness and remarkable results against noise while preserving edges and corners, compared to some well‐known models in literature. For a fair comparison, we manage to obtain the optimal parameters of every model thanks to PSNR and SSIM. We demonstrate the robustness efficacy of our model against high level of noise.

Effective hybridization approach for noise removal in magnetic resonance imaging

Effective hybridization approach for noise removal in magnetic resonance imaging

Qin Seal Script Character Recognition with Fuzzy and Incomplete Information

The dependable and efficient identification of Qin seal script characters is pivotal in the discovery, preservation, and inheritance of the distinctive cultural values embodied by these artifacts. This paper uses image histograms of oriented gradients (HOG) features and an SVM model to discuss a character recognition model for identifying partial and blurred Qin seal script characters. The model achieves accurate recognition on a small, imbalanced dataset. Firstly, a dataset of Qin seal script image samples is established, and Gaussian filtering is employed to remove image noise. Subsequently, the gamma transformation algorithm adjusts the image brightness and enhances the contrast between font structures and image backgrounds. After a series of preprocessing operations, the oriented gradient histograms (HOG) features are extracted from the images. During model training, different weights are assigned to classes with varying sample quantities to address the issue of class imbalance and improve the model's classification accuracy. Results show that the model achieves an accuracy of 95.30%. This research can help historians quickly identify and extract the text content on newly discovered Qin slip cultural relics, shortening the cycle of building a historical database.

A Study on Image Noise Removal Techniques for Magnetic Resonance Imaging

Medical image denoising is very important and challenging area in the field of image processing. MRI (Magnetic resonance imaging) is very popular and most effective imaging technique. During the acquisition, MR images affected by random noise can be modeled as Gaussian or Rician distribution. In last few decades, so many denoising techniques were proposed but there are some limitations with the algorithms, because image edges and fine details are need to preserve. So there is need of compromise between de-noising quality and edge preservation to use images for real time application. Computation time is also very important parameter to implement the algorithms. In this paper, we have done an overview of different denoising algorithm. It observes that NLM (Non-Local Means) filter is much better than other existing state of art methods. Here study is done for enhancement of NLM to improve the performance. Results of different algorithms show that PCA (Principal Component Analysis) based algorithm with NLM performs much better in both quantitative and qualitative manner.