Image Denoising Research Articles

Early Breast Cancer Detection Based on Locally Available Mammography Data Using Convolutional Neural Network

In this work, convolutional neural network model was developed to read mammography image scans to predict malignancy. Firstly, mammography image data was sourced locally from University of Abuja Teaching hospital, Gwagwalada, Nigeria and then combined with publicly available mammography image dataset from The Mammographic Image Analysis Society (MIAS) database. The data obtained were then preprocessed using Contrast Limited Adaptive Histogram Equalization (CLAHE), Image Denoising, Padding and Formatting. Transfer learning was implemented by re-architecting pre-trained VGG-16, VGG-19, MobileNet-V2 and DenseNet-121 models to have an output layer with two neurons and softmax activation function, each signifying the degree to which calcifications in the mammography image is benign or malignant. The model was then trained on the preprocessed data and evaluated, the following metrics were achieved: VGG-16 and VGG-19 based models achieved an accuracy of 86% each, precision of 87% and 88% respectively and recall of 92% and 93% respectively. MobileNet-V2 has accuracy of 90%, precision and recall of 93% while the DenseNet-121 model achieved an accuracy of 95%, precision of 93% and 100% score in recall. MobileNet-V2 performed best in the computational complexity analysis with 336.34 MFLOPs, followed by DenseNet-121 with 5.69GFLOPs. VGG-16 and VGG-19 have computational complexity of 15.3 GFLOPs and 19.6 GFLOPs respectively.

A Family of Generalized Cardinal Polishing Splines.

Spline functions have received widespread attention in the fields of image sampling and reconstruction. To enhance the performance of splines in reconstruction and reduce the computational burden of solving large linear equations, we propose a family of generalized cardinal polishing splines (GCP-splines) and provide a system of linear equations to obtain the expressions of GCP-splines. First, we propose a cardinal polishing spline basis function with high-precision. Then, we propose a class of GCP-splines and give a general theory of GCP-splines. To calculate the expressions of GCP-splines, we adopt a system of linear equations to obtain the time shifts operator and the convolutional coefficients based on the search spacing and number of terms. Finally, we propose continuous and discrete interpolation models based on GCP-splines, and demonstrate several valuable properties, such as order of approximation and the Riesz basis. To evaluate the performance of GCP-splines, we conduct several experiments on test images from different modalities. The experimental results demonstrate that the GCP-splines for image interpolation and image denoising have better performance and outperform other methods.

Single Stage Adaptive Multi-Attention Network for Image Restoration.

Recently attention-based networks have been successful for image restoration tasks. However, existing methods are either computationally expensive or have limited receptive fields, adding constraints to the model. They are also less resilient in spatial and contextual aspects and lack pixel-to-pixel correspondence, which may degrade feature representations. In this paper, we propose a novel and computationally efficient architecture Single Stage Adaptive Multi-Attention Network (SSAMAN) for image restoration tasks, particularly for image denoising and image deblurring. SSAMAN efficiently addresses computational challenges and expands receptive fields, enhancing robustness in spatial and contextual feature representation. Its Adaptive Multi-Attention Module (AMAM), which consists of Adaptive Pixel Attention Branch (APAB) and an Adaptive Channel Attention Branch (ACAB), uniquely integrates channel and pixel-wise dimensions, significantly improving sensitivity to edges, shapes, and textures. We perform extensive experiments and ablation studies to validate the performance of SSAMAN. Our model shows state-of-the-art results on various benchmarks, for example, on image denoising tasks, SSAMAN achieves a notable 40.08 dB PSNR on SIDD dataset, outperforming Restormer by 0.06 dB PSNR, with 41.02% less computational cost, and achieves a 40.05 dB PSNR on the DND dataset. For image deblurring, SSAMAN achieves 33.53 dB PSNR on GoPro dataset. Code and models are available at Github.

Laplacian Gradient Consistency Prior for Flash Guided Non-Flash Image Denoising.

For flash guided non-flash image denoising, the main challenge is to explore the consistency prior between the two modalities. Most existing methods attempt to model the flash/non-flash consistency in pixel level, which may easily lead to blurred edges. Different from these methods, we have an important finding in this paper, which reveals that the modality gap between flash and non-flash images conforms to the Laplacian distribution in gradient domain. Based on this finding, we establish a Laplacian gradient consistency (LGC) model for flash guided non-flash image denoising. This model is demonstrated to have faster convergence speed and denoising accuracy than the traditional pixel consistency model. Through solving the LGC model, we further design a deep network namely LGCNet. Different from existing image denoising networks, each component of the LGCNet strictly matches the solution of LGC model, giving the network good interpretability. The performance of the proposed LGCNet is evaluated on three different flash/non-flash image datasets, which demonstrates its superior denoising performance over many state-of-the-art methods both quantitatively and qualitatively. The intermediate features are also visualized to verify the effectiveness of the Laplacian gradient consistency prior. The source codes are available at https://github.com/JingyiXu404/LGCNet.

A Novel Approach for Single-Shot Target Recognition by Matching Graph of Feature Pixels

Image feature extraction and matching constitute fundamental steps in computer vision and image analysis, enabling various applications ranging from object recognition to scene reconstruction. Object recognition is one of the major applications of artificial intelligence. Since last two decades, different techniques for object recognition were given by researchers from across the world. This paper presents a novel approach for automatic target recognition in autonomous weapons by matching connected graph of feature pixels. The Moving and Stationary Target Acquisition and Recognition (MSTAR) is taken for experimental purpose. The Proposed model has three stages for a target recognition; Image Denoising, Feature extraction & graph generation, and feature matching. Firstly, a sample image of targets taken from the MSTAR is taken as an input. Further, input sample is de-noised and features are extracted using Speeded-Up Robust Features (SURF) algorithm. Thereafter, a graph is created using feature pixels and this complete process is repeated with test image. Further, both graphs are compared using a proposed feature graph matching algorithm which results an recognition accuracy score. Proposed model is tested over the MSTAR dataset and it resulted in varying accuracy at different feature matching thresholds.

Performance analysis of the convex non-convex total variation denoising model

<p>Total variation (TV) regularization is a powerful tool in image denoising, but it often exhibits limited performance in preserving edges. In contrast, non-convex TV regularization can more effectively preserve edges and contours, albeit posing challenges when solving. Recently, the convex non-convex (CNC) strategy has emerged as a potent approach that allows incorporating non-convex TV regularization terms while maintaining the overall convexity of the objective function. Its superior performance has been validated through various numerical experiments; however, theoretical analysis remains lacking. In this paper, we provided theoretical analysis of the performance of the CNC-TV denoising model. By utilizing the oracle inequality, we derived an improved upper bound on its performance compared to TV regularization. In addition, we devised an alternating direction method of multipliers (ADMM) algorithm to address the proposed model and verified its convergence properties. Our proposed model has been validated through numerical experiments in 1D and 2D denoising, demonstrating its exceptional performance.</p>

Wavelet-based Denoising of Magnetic Resonance Images Using Optimized Exponential Function Thresholding and Wiener Filter

Wavelet-based Denoising of Magnetic Resonance Images Using Optimized Exponential Function Thresholding and Wiener Filter

Effective Denoising of InSAR Phase Images via Compressive Sensing

Effective Denoising of InSAR Phase Images via Compressive Sensing

Local Self-Similar Solution of ADMM for Denoising of Retinal OCT Images

Local Self-Similar Solution of ADMM for Denoising of Retinal OCT Images

Variational approach to simultaneous fusion and denoising of color images with different spatial resolution

Variational approach to simultaneous fusion and denoising of color images with different spatial resolution

A multi-tasking novel variational model for image decolorization and denoising

Image decolorization has a wide range of applications in digital printing, photo rendering and single-channel image processing. During the process of image decolorization, it is significant to retain more contrast information and avoid increasing the noise level of the original image. As we know, noise was hardly considered in previous decolorization methods. In this paper, we propose a new multi-tasking variational model with two tasks: decolorizing and denoising. The first task is image decolorization, that is, to transform color images to grayscale images. The other task is to control the noise level, and even eliminate the noise. Inspired by TV-Stokes model, we introduce the unit normal vector containing the basic geometry information of the color image in the model. The existence and the uniqueness of the solution to the variational problems are proved. For the variational model, two numerical methods are adopted. At first, we use gradient descent method to obtain evolution equations and construct finite difference schemes to solve the corresponding equations of proposed variational model. Furthermore, an efficient augmented Lagrangian approach is applied to solve the multi-tasking variational model directly. For noisy images and clean images, we employ two output patterns of grayscale images respectively. Especially, our model is stable for the noisy images. Numerical results and comparisons show that the proposed model can produce highly competitive results in terms of decolorization quality of noisy images, edge preservation and adjustment of parameters.

Flex-DLD: Deep Low-Rank Decomposition Model With Flexible Priors for Hyperspectral Image Denoising and Restoration.

Hyperspectral images (HSIs) are composed of hundreds of contiguous waveband images, offering a wealth of spatial and spectral information. However, the practical use of HSIs is often hindered by the presence of complicated noise caused by various factors such as non-uniform sensor response and dark current. Traditional methods for denoising HSIs rely on constrained optimization approaches, where selecting appropriate prior knowledge is critical for achieving satisfactory results. Nevertheless, these traditional algorithms are limited by hand-crafted priors, leaving room for improvement in their denoising performance. Recently, the supervised deep learning technique has emerged as a promising approach for HSI denoising. However, their requirement for paired training data and poor generalization ability on untrained noise distributions pose challenges in practical applications. In this paper, we design a novel algorithm by the synergism of optimization-based methods and deep learning techniques. Specifically, we introduce a plug-and-play Deep Low-rank Decomposition (DLD) model into the optimization framework. Furthermore, we propose an effective mechanism to incorporate traditional prior knowledge into the DLD model. Finally, we provide a detailed analysis of the optimization process and convergence of the proposed method. Empirical evaluations on various tasks, including hyperspectral image denoising and spectral compressive imaging, demonstrate the superiority of our approach over state-of-the-art methods.

Self-Augmented Noisy Image for Noise2Noise Image Denoising

Self-Augmented Noisy Image for Noise2Noise Image Denoising

Joint Training of Noisy Image Patch and Impulse Response of Low-Pass Filter in CNN for Image Denoising

Joint Training of Noisy Image Patch and Impulse Response of Low-Pass Filter in CNN for Image Denoising

RSID-CR: Remote Sensing Image Denoising Based on Contrastive Learning

RSID-CR: Remote Sensing Image Denoising Based on Contrastive Learning

An Efficient Multiscale Spatial Rearrangement MLP Architecture for Image Restoration.

The effective use of long-range information can yield improved network performance, which is very important for image restoration. Although local window-based models have linear complexity and can be feasibly applied to process high-resolution images, a single-scale window has a limited receptive field and is less efficient for encoding long-range context information. To address this issue, this paper presents a single-stage multiscale spatial rearrangement multilayer perceptron (MSSR-MLP) architecture that can obtain information at different scales within a local window. Specifically, we propose a simple and efficient spatial rearrangement module (SRM) that moves information outside the local window to the inside of the local window so that long-range dependencies can be modeled using only a window-based fully connected (FC) layer. The SRM can extend the local receptive field of a window-based FC layer without introducing additional parameters and FLOPs. Utilizing several spatial rearrangement modules with different step sizes, we design an efficient multiscale spatial rearrangement MLP architecture for image restoration. This design aggregates multiscale information to achieve improved restoration quality while maintaining a low computational cost. Extensive experiments conducted on several image restoration tasks demonstrate the efficiency and effectiveness of our method. For example, it requires only ~4.3% of the FLOPs needed by SwinIR for Gaussian gray image denoising, ~13.9% of the FLOPs needed by C2 PNet for single-image dehazing and ~18.9% of the FLOPs needed by MAXIM for single-image motion deblurring but achieves better performance on each of these restoration tasks.

LG-DBNet: Local and Global Dual-Branch Network for SAR Image Denoising

LG-DBNet: Local and Global Dual-Branch Network for SAR Image Denoising

Application of Convolutional Neural Networks for Parallel Multi-Scale Feature Extraction in Noise Image Denoising

Application of Convolutional Neural Networks for Parallel Multi-Scale Feature Extraction in Noise Image Denoising

Double stranded smoothing technique in energy minimization problems

<p style='text-indent:20px;'>In this study, we propose a new algorithm for image denoising by using double stranded Bezier curves based smoothing technique. The algorithm is developed to solve energy minimization problems with non-smooth total variation norm (TV). In order to demonstrate the efficiency of the algorithm we apply it to some numerical examples. The experimental results are reported and they are compared with the results obtained from TV algorithm with hyperbolic smoothing and standard TV algorithm.</p>

SEMNet: Deep Synthetic Training for Unprecedented SEM Image Denoising and Super-Resolution

SEMNet: Deep Synthetic Training for Unprecedented SEM Image Denoising and Super-Resolution