Image Smoothing Research Articles

Guided adaptive interpolation filter

Edge-aware smoothing has proved to be a fundamental technique for various image processing and computer vision tasks. In this study, the authors introduce a local, non-iterative, and effective edge-preserving filter namely guided adaptive interpolation filter (GAIF). GAIF can be used as a post-processing step after any smoothing filter to improve its edge preservation performance without reformulation. GAIF has an computation complexity, where N is the total number of pixels in the image. To further increase the efficiency of GAIF at edge-preservation, two techniques are introduced and demonstrated. GAIF efficiency is demonstrated and compared to state-of-the-art techniques on a number of tasks including image smoothing, flash/no-flash image denoising/fusion, single image dehazing, and image details enhancement.

Bayer image demosaicking and denoising based on specialized networks using deep learning

Demosaicking is the way toward reproducing a full hued picture from a deficient shaded picture. The single sensor doesn't catch all hues for a single pixel. To address this, a color filter array (CFA) is utilized to get a hued picture from a single sensor. The created picture from CFA is called a mosaic picture. In this research, we utilize specialized networks to remove the noise from Bayer images. The mosaic picture is adulterated by commotion presented by a sensor or other equipment during catching. Demosaicking on the boisterous mosaic picture makes antiquities, for example, moire and zippering. Some solutions have been proposed for denoising mosaic images but they are handcrafted solutions. In this paper, a solution is proposed to the first denoise and then demosaic the image using machine learning. The mosaic image is denoised using CNN which is then demosaicked using the residual learning strategy of a single specialized network. One of the networks is DHTN (deep high textured network) which is trained on textured images and the second one is DSTN (deep smooth textured network) which is trained on smooth images. Preliminary results show that the proposed approach generates better results and higher quality images than traditional approaches.

Combinatorial active contour bilateral filter for ultrasound image segmentation.

.Purpose: Utilization of computer-aided diagnosis (CAD) on radiological ultrasound (US) imaging has increased tremendously. The prominent CAD applications are found in breast and thyroid cancer investigation. To make appropriate clinical recommendations, it is important to accurately segment the cancerous object called a lesion. Segmentation is a crucial step but undoubtedly a challenging problem due to various perturbations, e.g., speckle noise, intensity inhomogeneity, and low contrast.Approach: We present a combinatorial framework for US image segmentation using a bilateral filter (BF) and hybrid region-edge-based active contour (AC) model. The BF is adopted to smooth images while preserving edges. Then the hybrid model of region and edge-based AC is applied along the scales in a global-to-local manner to capture the lesion areas. The framework was tested in segmenting 258 US images of breast and thyroid, which were validated by manual ground truths.Results: The proposed framework is accessed quantitatively based on the overlapping values of the Dice coefficient, which reaches . The evaluation with and without the BF shows that the enhancement procedure improves the framework well.Conclusions: The high performance of the proposed method in our experimental results indicates its potential for practical implementations in CAD radiological US systems.

Reconstruction-based 6D pose estimation for robotic assembly.

Pose estimation is important for many robotic applications including bin picking and robotic assembly and collaboration. However, robust and accurate estimation of the poses of industrial objects is a challenging task owing to the various object shapes and complex working environments. This paper presents a method of estimating the poses of narrow and elongated industrial objects with a low-cost RGB-D (depth and color) camera to guide the process of robotic assembly. The proposed method comprises three main steps: reconstruction involved in preprocessing, pose initialization with geometric features, and tracking aided by contour cues. Pose tracking is coupled with real-time dense reconstruction, which can synthesize a smooth depth image as a substitute for the raw depth image. Because industrial objects (e.g., fork and adapter) feature mostly planar structures, primitive geometric features, such as three-dimensional planes, are extracted from the point cloud and utilized to induce a promising initial pose. For robust tracking of the adapter consisting of narrow and elongated planes, the dense surface correspondences are combined with sparse contour correspondences in the refinement scheme. This combination allows for a satisfactory tolerance to the initial guess in the pose tracking phase. The experimental results demonstrate the feasibility of the proposed method.

Blind deconvolution via complementarily structure-aware image smoothing

Blind deconvolution is known as a challenging low-level vision problem due to the diverse blur scenarios in real-world imaging. Another attempt is made with critical thoughts on existing image priors for nonparametric blur kernel estimation, proposing an alternative approach to blind deconvolution via complementarily structure-aware image smoothing (CSIS). Similar to most state-of-the-art blind deblurring methods, the proposed approach partly builds on the naive L0-based sparse model, but the core contribution here is to additionally advocate a type of redescending potential (RDP) functions as a more elegant way for boosting blind deblurring. With the RDP element, the new approach is capable of easily achieving the discrimination between clear and blurred images. Meanwhile, the performance of the proposed method can be better ensured due to the complementary smoothing behavior induced by the RDP functions. Specifically speaking, it is known that L0-based smoothing plays a critical role in pursuing salient step-edges from blurry images especially in the early period of blur kernel estimation, whereas the RDP-based smoothing is found particularly significant in the later stage when the fine pursuit of salient roof-edges or ramp-edges are dominantly critical for more precise and robust kernel estimation. In this sense, the proposed CSIS-based blind deblurring algorithm is more intuitive than previous L0-based methods. Not only that, numerous experimental results on benchmark blurred images, no matter synthetic or realistic, also demonstrate the comparable or even better performance of the proposed approach in terms of both effectiveness and robustness.

Separable reversible data hiding in encrypted images based on flexible preservation of the differences

To better protect the security of users’ private data in the cloud environment, the technology for separable reversible data hiding in encrypted images has been attracting increasing attention from researchers. In this paper, we propose a separable reversible data hiding scheme in encrypted images based on the flexible preservation of differences. This scheme has three parts: 1) For the content owner, the original image is divided into non-overlapping blocks, for which block-mean is computed. Then the differences between the values of every pixel and the block-mean are obtained and an initial label map is generated. Because most of the differences tend to concentrate around 0, we use two bits to dynamically record the range of the differences to vacate space for hiding. Further, introducing the block-mean differences also serves to vacate more space, for which the label map is amended accordingly. Finally, the image with free space is encrypted into the encrypted image using an encryption key. 2) For the data hider, the secret bits are embedded into the encrypted image by directly replacing the spare bits without obtaining any information of the original image. 3) For the receiver, he/she can achieve the desired information according to the key in his/her possession. Experimental results show that our proposed scheme is able to achieve an average embedding capacity as large as 1.785 bpp and 1.709 bpp when block size is set to 2 × 2 and 2 × 4, respectively. Comparison with those of previous schemes, the proposed scheme has excellent embedding capacity, especially for smoother images.

Image Segmentation and Denoising Algorithm Based on Partial Differential Equations

The image denoising algorithm based on partial differential equation can selectively solve the problem of noise and image smoothing, which has become the focus of current image denoising algorithms. In the research of image segmentation, this paper proposes a new image segmentation model, which not only can segment multi-phase images, but also achieves simple implementation, which greatly saves the time required for segmentation. The total variation denoising model is analyzed to improve the fidelity of the model: (1) The total variation and the image denoising model, which constructs a trend fidelity term that can effectively suppress the “staircase effect”; 2) Wavelet transform image denoising model based on curvature variation, which uses a horizontal set of enhanced images to establish a curvature drive function based on the horizontal set curvature, and then introduces the curvature drive function as a correction factor into the variational model to establish the curvature change. The experimental results show that the two improved Partial differential equation image segmentation models (PDEISM) have obvious denoising effects, good visual effects and strong comprehensive performance. The simulation experiment was carried out with MATLAB software to verify the superiority of the improved algorithm. The conclusions drawn from the final simulation experiment are consistent with the conclusions of the theoretical analysis, which indicates that the improved algorithm is superior to the traditional algorithm in overall performance and has better practical application value.

Zero norm based analysis model for image smoothing and reconstruction

The sparsity-based approaches have demonstrated promising performance in image processing. In this paper, for better preservation of the salient edge structures of images, we propose an ℓ 0 + ℓ 2-norm based analysis model， which requires solving a challenging non-separable ℓ 0-norm related minimization problem, and we also propose an inexact augmented Lagrangian method with proven convergence to a local minimum. Extensive experiments in image smoothing, including texture removal and context smoothing, show that our method achieves better visual results over various sparsity-based models and the CNN method. Also, experiments on sparse view CT reconstruction further validate the advantage of the proposed method.

Anisotropic Diffusion Combined with Nonconvex Functional for Noise Image Segmentation

Aiming at the problems of low segmentation accuracy of noise image, poor noise immunity of the existing models and poor adaptability to complex noise environment, a noise image segmentation algorithm using anisotropic diffusion and nonconvex functional was proposed. First, focusing on the “staircase effect”, a nonconvex functional was introduced into the energy functional model for smooth denoising. Second, the validity and accuracy of the model were established by proving that there was no global minimum in the solution space of the nonconvex energy functional model; the improved model was then used to obtain a smooth and clear image edge while maintaining the edge integrity. Third, the smooth image obtained from the nonconvex energy functional model was combined with the level set model to obtain the anisotropic diffusion gray level set model. The optimal outline of the target was obtained by calculating the minimum value of the energy functional. Finally, an anisotropic diffusion equation with nonconvex energy functional model was built in this algorithm to segment noise image accurately and quickly. A series of comparative experiments on the proposed algorithm and similar algorithms were conducted. The results showed that the proposed algorithm had strong noise resistance and provided precise segmentation for noise image.

Domain transform for spherical geometry images

This work presents the Domain Transform for Spherical Geometry Images (DT-SGIM): a filter designed for smoothing triangle meshes represented as spherical geometry images (SGIM) and, at the same time, preserving the meshes’ important curvatures. It is based on the domain transform filter (DT), a high-performance, real-time algorithm for edge-aware smoothing of color images. Our main contribution is the extension of the DT filter to the SGIM domain, resulting in the first method specifically designed for filtering 3-D objects represented by SGIMs, as well as the first demonstration of a domain transform-based filter applied to the problem of polygonal mesh filtering.

Multimodal medical image fusion via laplacian pyramid and convolutional neural network reconstruction with local gradient energy strategy

BackgroundIn recent years, numerous fusion algorithms have been proposed for multimodal medical images. The Laplacian pyramid is one type of multiscale fusion method. Although the pyramid-based fusion algorithm can fuse images well, it has the disadvantages of edge degradation, detail loss and image smoothing as the number of decomposition layers increase, which is harmful for medical diagnosis and analysis. MethodThis paper proposes a medical image fusion algorithm based on the Laplacian pyramid and convolutional neural network reconstruction with local gradient energy strategy, which can greatly improve the edge quality. First, multimodal medical images are reconstructed through convolutional neural network. Then, the Laplacian pyramid is applied in the decomposition and fusion process. The optimal number of decomposition layers is determined by experiments. In addition, a local gradient energy fusion strategy is utilized to fuse the coefficients in each layer. Finally, the fused image is output through Laplacian inverse transformation. ResultsCompared with existing algorithms, our fusion results represent better vision quality performance. Furthermore, our algorithm is considerably superior to the compared algorithms in objective indicators. In addition, in our fusion results of Alzheimer and Glioma, the disease details are much clearer than those of compared algorithms, which can provide a reliable basis for doctors to analyze disease and make pathological diagnoses.

Structure-preserving image smoothing with semantic cues

The purpose of image smoothing is to smooth out low-contrast textures while preserving meaningful structures. Although this problem has been studied for decades, it still leaves a lot of space to improve. Recently, learning-based edge detectors have superior performance to traditional manually-designed detectors. Based on the edge detection technique, we present a novel optimization-based image smoothing model combining semantic prior and perform $$L_0$$ gradient minimization recursively in our framework to refine the result. Our framework combines the advantage of the state-of-the-art edge detector and the ability of $$L_0$$ gradient minimization for structure-preserving image smoothing. Moreover, we employ a large number of real-world images and perform various experiments to evaluate our algorithm. Experimental results show that our algorithm outperforms state-of-the-art algorithms, especially in extracting subjectively-meaningful structures.

Design and Implementation of Marine Automatic Target Recognition System Based on Visible Remote Sensing Images

Gao, X.B., 2020. Design and implementation of marine automatic target recognition system based on visible remote sensing images. In: Bai, X. and Zhou, H. (eds.), Advances in Water Resources, Environmental Protection, and Sustainable Development. Journal of Coastal Research, Special Issue No. 115, pp. 277-279. Coconut Creek (Florida), ISSN 0749-0208.In the process of visible light remote sensing imaging, different sea surface wave conditions have different reflection ability to illumination, which makes the brightness, contrast and other information of visible light remote sensing images have great changes. Marine ship target recognition is an important application of image processing in remote sensing field. Based on pixel-level fusion of visible and infrared bispectral images, this paper extracts features from fusion results, and recognizes targets in images according to the matching of target image features and template image features. Comprehensive utilization of image preprocessing, image smoothing and anti-cloud interference algorithms can quickly and accurately realize ship target detection under the complex background of land and sea. The experimental results show that the algorithm is effective both in physical mechanism and in actual image effect.

Non-blind and Blind Deconvolution Under Poisson Noise Using Fractional-Order Total Variation

In a wide range of applications such as astronomy, biology, and medical imaging, acquired data are usually corrupted by Poisson noise and blurring artifacts. Poisson noise often occurs when photon counting is involved in such imaging modalities as X-ray, positron emission tomography, and fluorescence microscopy. Meanwhile, blurring is also inevitable due to the physical mechanism of an imaging system, which can be modeled as a convolution of the image with a point spread function. In this paper, we consider both non-blind and blind image deblurring models that deal with Poisson noise. In the pursuit of high-order smoothness of a restored image, we propose a fractional-order total variation regularization to remove the blur and Poisson noise simultaneously. We develop two efficient algorithms based on the alternating direction method of multipliers, while an expectation-maximization algorithm is adopted only in the blind case. A variety of numerical experiments have demonstrated that the proposed algorithms can efficiently reconstruct piecewise smooth images degraded by Poisson noise and various types of blurring, including Gaussian and motion blurs. Specifically for blind image deblurring, we obtain significant improvements over the state of the art.

The Accuracy Trap: The Values and Meaning of Algorithmic Mapping, from Mineral Extraction to Climate Change

Abstract For specialists and non-specialists alike, maps are one of the central ways that the environment becomes visible and comprehensible. Since the 1960s, both the practice and the values of environmental mapping have been transformed by new algorithmic methods for turning point-by-point measurements into a smooth cartographic image, especially when visualising the invisible geographies of pollution, climate change, and underground resources. The resulting maps are now ubiquitous. This article argues that algorithmic methods - especially those created by a small group of French mining engineers and installed widely in software by the 1990s - shifted the values and meaning of environmental mapping away from a traditional concern with qualitative realism to a new emphasis on quantitative accuracy . This was a shift not just in the goals of mapping but in the kind of environment that maps ultimately construct. The prioritisation of accuracy should therefore not be seen as a straightforward improvement, as its associated values raise difficult conceptual problems, both historical and historiographic, about scale, expertise and the role of human judgement in the creation of environmental fact. Historicising the techniques and meanings of mapping is especially important as environmental historians consider new geospatial methods - including algorithmic methods - in their own work.

Blind image deblurring based on the sparsity of patch minimum information

Blind image deblurring is a very challenging inverse problem due to the severe ill-posedness caused by the unknown kernel and the latent clear image. To tackle this problem, appropriate smoothing regularizations and image priors are usually employed and incorporated into the associated variational models to alleviate the inherent ill-posedness. In this paper, we first propose a strongly imposed zero patch minimum constraint for the latent image, which helps alleviate the ill-posedness of the inverse problem for blind image deblurring. Then, we retrieve important fine details by assigning the patch minimum information obtained from the blurred image back to the latent image to further enhance its structure. Finally, we introduce an adaptive regularizer which was shown to have significantly better edge-preserving property than the total variation regularizer for the image restoration of degraded images. Operator splitting techniques are used to accomplish an efficient numerical implementation of the proposed variational model. A number of numerical experiments and comparisons with some state-of-the-art methods are conducted to demonstrate the effective performance of the newly proposed method.

Robust Gaussian Noise Detection and Removal in Color Images using Modified Fuzzy Set Filter

Abstract In the data collection phase, the digital images are captured using sensors that often contaminated by noise (undesired random signal). In digital image processing task, enhancing the image quality and reducing the noise is a central process. Image denoising effectively preserves the image edges to a higher extend in the flat regions. Several adaptive filters (median filter, Gaussian filter, fuzzy filter, etc.) have been utilized to improve the smoothness of digital image, but these filters failed to preserve the image edges while removing noise. In this paper, a modified fuzzy set filter has been proposed to eliminate noise for restoring the digital image. Usually in fuzzy set filter, sixteen fuzzy rules are generated to find the noisy pixels in the digital image. In modified fuzzy set filter, a set of twenty-four fuzzy rules are generated with additional four pixel locations for determining the noisy pixels in the digital image. The additional eight fuzzy rules ease the process of finding the image pixels,whether it required averaging or not. In this scenario, the input digital images were collected from the underwater photography fish dataset. The efficiency of the modified fuzzy set filter was evaluated by varying degrees of Gaussian noise (0.01, 0.03, and 0.1 levels of Gaussian noise). For performance evaluation, Structural Similarity (SSIM), Mean Structural Similarity (MSSIM), Mean Square Error (MSE), Normalized Mean Square Error (NMSE), Universal Image Quality Index (UIQI), Peak Signal to Noise Ratio (PSNR), and Visual Information Fidelity (VIF) were used. The experimental results showed that the modified fuzzy set filter improved PSNR value up to 2-3 dB, MSSIM up to 0.12-0.03, and NMSE value up to 0.38-0.1 compared to the traditional filtering techniques.

Holistic modeling and analysis of multistage manufacturing processes with sparse effective inputs and mixed profile outputs

In a Multistage Manufacturing Process (MMP), multiple types of sensors are deployed to collect intermediate product quality measurements after each stage of manufacturing. This study aims at modeling the relationship between these quality outputs of mixed profiles and sparse effective process inputs. We propose an analytical framework based on four process characteristics: (i) every input only affects the outputs of the same and the later stages; (ii) the outputs from all stages are smooth functional curves or images; (iii) only a small number of inputs influence the outputs; and (iv) the inputs cause a few variation patterns on the outputs. We formulate an optimization problem that simultaneously estimates the effects of process inputs on the outputs across the entire MMP. An ADMM consensus algorithm is developed to solve this problem. This algorithm is highly parallelizable and can handle a large amount of data of mixed types obtained from multiple stages. The ability of this algorithm in estimations, selecting effective inputs, and identifying the variation patterns of each stage is validated with simulation experiments.

An efficient method for PET image denoising by combining multi-scale transform and non-local means

The diagnosis of dementia, particularly in the early stages is very much helpful with Positron emission tomography (PET) image processing. The most important challenges in PET image processing are noise removal and region of interests (ROIs) segmentation. Although denoising and segmentation are performed independently, but the performance of the denoising process significantly affects the performance of the segmentation process. Due to the low signals to noise ratio and low contrast, PET image denoising is a challenging task. Individual wavelet, curvelet and non-local means (NLM) based methods are not well suited to handle both isotropic (smooth details) and anisotropic (edges and curves) features due to its restricted abilities. To address these issues, the present work proposes an efficient denoising framework for reducing the noise level of brain PET images based on the combination of multi-scale transform (wavelet and curvelet) and tree clustering non-local means (TNLM). The main objective of the proposed method is to extract the isotropic features from a noisy smooth PET image using tree clustering based non-local means (TNLM). Then curvelet-based denoising is applied to the residual image to extract the anisotropic features such as edges and curves. Finally, the extracted anisotropic features are inserted back into the isotropic features to obtain an estimated denoised image. Simulated phantom and clinical PET datasets have been used in this proposed work for testing and measuring the performance in the medical applications, such as gray matter segmentation and precise tumor region identification without any interaction with other structural images like MRI or CT. The results in the experimental section show that the proposed denoising method has obtained better performance than existing wavelet, curvelet, wavelet-curvelet, non-local means (NLM) and deep learning methods based on the preservation of the edges. Qualitatively, a notable gain is achieved in the proposed denoised PET images in terms of contrast enhancement than other existing denoising methods.

Analysis of Effect of Noise Removal and Image Smoothing for Automated White Blood Cells Detection and Counting

White blood platelet recognition and checking is extremely basic platelet cells being wellness marker. A character's health is chosen utilizing all out platelet number. A substance of the platelet specifically the white platelet cells, a crimson platelets and cells layout a realm of the wellbeing. For recognizable proof and treatment of disorders like paleness, leukemia and etc. In research center, blood cell counting is performed with the guide of the utilization of hemocytometer and microscope blood images. This methodology offers flawed and questionable results that rely upon medicinal specialist ability. This endeavor is hard and monotonous. Expectation of the examinations to give a study on pc vision gadget that can find and estimate the wide assortment of white platelet cells inside the platelet test images utilizing images preparing calculations. In this paper considering the image handling for a checking of blood white platelets. image preparing calculations include five steps: input image, preprocessing, image improvement, feature extraction division, highlight extraction and checking calculation. The goal is to take a gander at the exceptional framework of WBC checking and Recognition of the research headings.