Preserve Edge Details Research Articles

Underwater image enhancement method based on adaptive attenuation-curve prior.

The attenuation (sum of absorption and scattering), which is caused by the dense and non-uniform medium, generally leads to problems of color degradation and detail loss in underwater imaging. In this study, we describe an underwater image enhancement method based on adaptive attenuation-curve prior. This method uses color channel transfer (CCT) to preprocess the underwater images, light smoothing, and wavelength-dependent attenuation to estimate water light and obtain the attenuation ratio between color channels, and estimates and refines the initial relative transmission of the channel. Additionally, the method calculates the attenuation factor and saturation constraints of the three color channels and generates an adjusted reverse saturation map (ARSM) to address uneven light intensity, after which the image is restored through water light and transmission estimation. Furthermore, we applied white balance fusion globally guided image filtering (G-GIF) technology to achieve color enhancement and edge detail preservation in the underwater images. Comparison experiments showed that the proposed method obtained better color and de-hazing effects, as well as clearer edge details, relative to current methods.

Performance Analysis of Image Denoising using Deep Convolutional Neural Network

A performance analysis of conventional Convolutional Neural Network (CNN) based denoising method is proposed. In this image denoising method, the contrast of images is adaptively enhanced. Generally, it is not possible to capture the imageswith good quality for all situations. Because they are captured in various light conditions.So, the captured images are suffered by noise, which results in poor perceived image quality. Thus, it is necessary to improve the quality of images with edge detail preservation as much as possible. The convolutional neural network model for low light image enhancement is already developed and is named as DnCNNs. Here, the performance analysis of image denoising using the DnCNNmodel is presented. The DnCNN implicitly removes the noise in the image. The simulation results afford better reference for application developers.

Speckle Noise Reduction in Sonar Image Based on Adaptive Redundant Dictionary

As acoustic waves are affected by the channel characteristics, such as scattering and reverberation when propagating in water, sonar images often exhibit speckle noise which will cause visual quality of the image to decrease. Therefore, denoising is a crucial preprocessing technique in sonar image applications. However, speckle noise is mainly caused by the sediment echo signals which are related to the background of seafloor sediment and can be obtained by prior modeling. Although deep learning-based denoising algorithms represent a research hotspot now, they are not suitable for such applications due to the high calculation amount and the large requirement of original images considering that sonar is carried by Autonomous Underwater Vehicles (AUVs) for collecting sonar images and performing calculation. In contrast, dictionary learning-based denoising method is more suitable and easier to be modeled. Compared with deep learning, it can greatly reduce the calculation amount and is more easily integrated into AUV systems. In addition, dictionary learning method based on image sparse representation can effectively achieve image denoising similarly. In order to solve the above problems, we propose a new adaptive dictionary learning method based on multi-resolution characteristics, which combines K-SVD dictionary learning with wavelet transform. Our method has the characteristics of dictionary learning and inherits the features of wavelet analysis as well. Compared with several classical methods, the proposed method is better at speckle noise reduction and edge detail preservation. At the same time, the calculation time is greatly reduced and the efficiency is significantly improved.

De-Noising of Corrupted Fluoroscopy Images Based on a New Multi-Line Algorithm

Fluoroscopic images are a field of medical images that depends on the quality of image for correct diagnosis; the main trouble is the de-nosing and how to keep the poise between degradation of noisy image, from one side, and edge and fine details preservation, from the other side, especially when fluoroscopic images contain black and white type noise with high density. The previous filters could usually handle low/medium black and white type noise densities, that expense edge, =fine details preservation and fail with high density of noise that corrupts the images. Therefore, this paper proposed a new Multi-Line algorithm that deals with high-corrupted image with high density of black and white type noise. The experiments achieved images with a high quality and effectively preserved edge and fine details against black and white type noise densities depending on Peak Signal to Noise Ratio (PSNR), Mean Squared Error (MSE), and Image Enhancement Factor (IEF) measures.

Complex morphological filtering algorithm based on multi-structure elements

Morphological filter is a kind of nonlinear filter. Based on the erosion and dilation operations a complex morphological filter was proposed with multi-structure elements. In order to protect the image detail effectively, a noise detect method was introduced firstly. The noise pixels are removed using the proposed filter, while signal pixels hold their pixel value and are left unprocessed. The experimental results demonstrate that the proposed algorithm has good filtering performance, which can preserve edge details effectively during denoising. For both gray image and binary image with salt & pepper noise pollution, the proposed algorithm is superior to the conventional morphological methods in the aspect of objective performance evaluation and subjective visual effect. Meanwhile, the proposed method can also be extended to the color image processing.

A hybrid feature preservation technique based on luminosity and edge based contrast enhancement in color fundus images

Color fundus image analysis for detecting the retinal abnormalities requires an improved visualization of image attributes with sufficient luminosity, contrast and accurate edge details. A hybrid technique based on singular value equalization using shearlet transform and adaptive gamma correction, followed by contrast limited adaptive histogram equalization (CLAHE) is proposed for the enhancement of luminosity and contrast in color fundus images. The low frequency components of the original and adaptive gamma transformed value channel in HSV color space obtained by applying shearlet transform are considered for singular value equalization. The high frequency components of the unchanged value channel, denoised using soft thresholding are applied while performing inverse shearlet transform. Luminosity component in L*a*b* colorspace is considered for performing CLAHE on the singular value equalized image. Subjective analysis is done based on visualization of the image attributes and the objective analysis is carried out based on the parameters such as Peak signal to noise ratio, entropy, feature similarity index, edge-based contrast measure, quality index and noise suppression measure. The simulation results evince superior noise performance, sufficient luminosity adjustment and improved contrast along with excellent edge detail preservation when compared with the existing state-of-the-art techniques.

A local contrast fusion based 3D Otsu algorithm for multilevel image segmentation

To overcome the shortcomings of 1D and 2D Otsu&#x02BC s thresholding techniques, the 3D Otsu method has been developed. Among all Otsu&#x02BC s methods, 3D Otsu technique provides the best threshold values for the multi-level thresholding processes. In this paper, to improve the quality of segmented images, a simple and effective multilevel thresholding method is introduced. The proposed approach focuses on preserving edge detail by computing the 3D Otsu along the fusion phenomena. The advantages of the presented scheme include higher quality outcomes, better preservation of tiny details and boundaries and reduced execution time with rising threshold levels. The fusion approach depends upon the differences between pixel intensity values within a small local space of an image; it aims to improve localized information after the thresholding process. The fusion of images based on local contrast can improve image segmentation performance by minimizing the loss of local contrast, loss of details and gray-level distributions. Results show that the proposed method yields more promising segmentation results when compared to conventional 1D Otsu, 2D Otsu and 3D Otsu methods, as evident from the objective and subjective evaluations.

Infrared stripe correction algorithm based on wavelet decomposition and total variation-guided filtering

Stripe non-uniformity severely affects the quality of infrared images. It is challenging to remove stripe noise in low-texture images without blurring the details. We propose a single-frame image stripe correction algorithm that removes infrared noise while preserving image details. Firstly, wavelet transform is used for multi-scale analysis of the image. At the same time, Total variation model is used for small window to smooth the original image. The small-scale total variation model can well preserve the edge information of the image, but it will leave stripe noise. Therefore, according to the prior knowledge of the vertical component of the stripe noise, the spatial filtering is finally performed: the smoothed image is used as the guide image for the stripe noise denoising. It is possible to prevent the lead filter from mistaking the strong stripe noise as edge detail, resulting in corrected image residual streak noise. The algorithm is systematically evaluated by experiments on simulated images and original infrared images, as well as compared with the current advanced infrared stripe non-uniformity correction algorithms. It is proved that our algorithm can better eliminate stripe noise and preserve edge details.

Skin lesion segmentation using high-resolution convolutional neural network

Background and ObjectiveSkin lesion segmentation is an important but challenging task in computer-aided diagnosis of dermoscopy images. Many segmentation methods based on convolutional neural networks often fail to extract accurate lesion boundaries because the spatial size of feature maps decreases as the maps are processed throughout the network layers. We propose skin lesion segmentation in dermoscopy images based on a convolutional neural network with an attention mechanism, which can preserve edge details. MethodsWe devised a high-resolution feature block containing three branches, namely, main, spatial attention, and channel-wise attention branches. The main branch takes high-resolution feature maps as input to extract spatial details around boundaries. The other two attention branches boost the discriminative features in the main branch regarding the spatial and channel-wise dimensions. By fusing the branch outputs, robust features with detailed spatial information can be extracted, and accurate skin lesion boundaries can be obtained. ResultsExperiments on datasets from the International Symposium on Biomedical Imaging in 2016 and 2017 and the PH2 dataset retrieved Jaccard indices of 0.783, 0.858, and 0.857, respectively, for the proposed method. Hence, our method can accurately extract skin lesion boundaries and is robust to hair fibers and artifacts in the images. Overall, our method outperforms two typical segmentation networks (FCN-8 s and U-Net) and other state-of-the-art skin lesion segmentation methods. ConclusionsThe proposed network endowed with high-resolution feature blocks preserves spatial details during feature extraction, and its attention mechanism enhances representative features while suppressing noise. Hence, the proposed approach provides high-performance skin lesion segmentation.

A SAR Image Despeckling Method Using Multi-Scale Nonlocal Low-Rank Model

Speckle noise is an inherent nature of synthetic aperture radar (SAR) images, which degrades the quality of the images and makes the interpretation of SAR images difficult. In this letter, we propose a despeckling method by simultaneously exploring low-rank prior and multi-scale prior of SAR images. Especially, we propose a low-rank minimization model by considering a data fidelity term derived from the Fisher–Tippett distribution and a weighted nuclear norm regularization term. Furthermore, we explore the multi-scale prior by selecting similar patches from different scales of the SAR image. The resulting optimization problem is solved by the alternating direction method of multipliers (ADMM). Experiments conducted on both simulated and real SAR images demonstrate that the proposed method can provide promising despeckling results in terms of speckle reduction and texture and edge details preservation.

Adaptive Generalized Likelihood Ratio Test for Change Detection in SAR Images

Generalized likelihood ratio test (GLRT) was widely used in the change detection of synthetic aperture radar (SAR) images and employed as a competitive method in many studies. GLRT is applied to the neighborhood areas corresponding to the same geographical area in multitemporal SAR images. Generally, the difference image is acquired by applying the GLRT on a moving window empirically determined by the user, which is clearly unreasonable because the different pixels in an image may have different optimal windows. To overcome this drawback, we propose an adaptive GLRT (AGLRT) method for change detection in SAR images. AGLRT adaptively selects the homogeneity window for each pixel and can decrease the uncertainty of change detection due to manual selection of the moving window. Experiments are conducted on two data sets to confirm the performance of the proposed method. Experimental results show that AGLRT can both suppress the influence of noise and preserve edge details and improve the accuracy of change detection.

Deep Pyramid Generative Adversarial Network With Local and Nonlocal Similarity Features for Natural Motion Image Deblurring

It is of great importance to capture long-range dependency in image deblurring based on deep learning. Existing methods often capture long-range dependency by a large receptive field, which contributes by deep stacks of local convolutional operations. Therefore, it restricts network representation ability and causes unpleasant artifacts of restored images. In this paper, we propose a deep pyramid generative adversarial network with local and nonlocal similarity features, called LNL-PGAN, for natural motion image deblurring. First, we propose a nonlocal feature block as an essential component of the pyramid generator for obtaining nonlocal similarity features at multiple levels. Second, we design a local feature block as another essential component to make a great balance between local and nonlocal similarity features. The local and nonlocal feature blocks capture meaningful short-range and long-range dependencies in the pyramid generator to increase network representation ability. Third, we design a multiscale generative adversarial loss to preserve edge details and facilitate sharp edge prediction of restored images, and we introduce a multistage training strategy to facilitate network training, which can further improve the quality of the restored image. Extensive experimental results demonstrate that the proposed method yields superior performance against state-of-the-art methods on natural motion image deblurring in terms of visual quality and objective index, and it can be used as a unified network for single and dynamic motion image deblurring.

An adaptive edge-detection method based on histogram

In order to solve the problems of poor adaptability when setting threshold and the high probability of detecting pseudo-edges in the existing methods of edge detection, the paper proposes an adaptive edge-detection method based on histogram. Multi-scale wavelet transform is used to preprocess the image, the image details are highlighted obviously, and it also can avoid the effect of manual setting filter coefficients. Difference of gray values between the pixels of local area are used to calculate the gradients comprehensively, it extends the gradient direction to four directions. When calculating the gradient of edge pixel, the four directions make the expression of the gradients of edge points more perfect and avoid the edge points missing. The adaptive method is used to compute the threshold of edge-detection, the image is represented by histogram. Then use the ratio of the number of pixels in the bar and the total numbers of pixels to set the initial threshold. The regions on both sides of the initial threshold are used to calculate the high threshold and low threshold until the reasonable error between the current threshold and the previous threshold is very small iteratively. The acquired threshold makes the self-adaptability more reasonable and stronger, it also avoids the detection errors, the connection errors and the pseudo-edges which are caused by setting threshold artificially. The experimental results show that the proposed algorithm of edge detection has a good effect of preserving edge detail and filtering noise of image.

Iterative quality enhancement via residual-artifact learning networks for low-dose CT

Radiation exposure and the associated risk of cancer for patients in computed tomography (CT) scans have been major clinical concerns. The radiation exposure can be reduced effectively via lowering the x-ray tube current (mA). However, this strategy may lead to excessive noise and streak artifacts in the conventional filtered back-projection reconstructed images. To address this issue, some deep convolutional neural network (ConvNet) based approaches have been developed for low-dose CT imaging inspired by the recent development of machine learning. Nevertheless, some of the image textures reconstructed by the ConvNet could be corrupted by the severe streaks, especially in ultra-low-dose cases, which could be close to prostheses and hamper diagnosis. Therefore, in this work, we propose an iterative residual-artifact learning ConvNet (IRLNet) approach to improve the reconstruction performance over the ConvNet based approaches. Specifically, the proposed IRLNet estimates the high-frequency details within the noise and then removes them iteratively; after eliminating severe streaks in the low-dose CT images, the residual low-frequency details can be processed through the conventional network. Moreover, the proposed IRLNet scheme can be extended for robust handling of quantitative dual energy CT/cerebral perfusion CT imaging, and statistical iterative reconstruction. Real patient data are used to evaluate the proposed IRLNet, and the experimental results demonstrate that the proposed IRLNet approach outperforms the previous ConvNet based approaches in reducing the image noise and streak artifacts efficiently at the same time as preserving edge details well, suggesting that the proposed IRLNet approach can be used to improve the CT image quality, especially in ultra-low-dose cases.

Deep Color Guided Coarse-to-Fine Convolutional Network Cascade for Depth Image Super-Resolution.

Depth image super-resolution is a significant yet challenging task. In this paper, we introduce a novel deep color guided coarse-to-fine convolutional neural network (CNN) framework to address this problem. First, we present a datadriven filter method to approximate the ideal filter for depth image super-resolution instead of hand-designed filters. Based on large data samples, the filter learned is more accurate and stable for upsampling depth image. Second, we introduce a coarse-to-fine CNN to learn different sizes of filter kernels. In coarse stage, larger filter kernels are learned by CNN to achieve crude high-resolution depth image. As to fine stage, the crude high-resolution depth image is used as the input so that smaller filter kernels are learned to gain more accurate results. Benefit from this network, we can progressively recover the high frequency details. Third, we construct a color guidance strategy that fuses color difference and spatial distance for depth image upsampling. We revise the interpolated high-resolution depth image according to the corresponding pixels in highresolution color maps. Guided by color information, the depth of high-resolution image obtained can alleviate texture copying artifacts and preserve edge details effectively. Quantitative and qualitative experimental results demonstrate our state-of-the-art performance for depth map super-resolution.

A three stage integrated denoising approach for grey scale images

Restoration of the images contaminated with Additive White Gaussian Noise (AWGN) is a fundamental operation at the interface of statistical and functional analysis. It is an extremely exigent job to design a noise suppression algorithm with edge and feature detail preservation owing to random manifestation of the noise amongst the pixels. In spite of documentation of the sophisticated denoising algorithms in literature, a desired amount of applicability is not achieved as they leave residual noise, create artifacts and remove fine structures. In this paper we propose an integrated image denoising algorithm which exploits the bit plane slicing based image decomposition approach for residual noise removal especially at higher noise levels. The intuitive idea of segregation of noise in decomposed bit planes and selective criterion of denoising lower order bit planes with adaptive bitonic filtering is able to preserve feature details while showing strong denoising performance. Experimental analysis shows that the proposed methodology gives considerable results at low noise values and outperforms other existing techniques at higher noise values both in terms of qualitative and quantitative performance.

A Spatial-Temporal Adaptive Neighborhood-Based Ratio Approach for Change Detection in SAR Images

The neighborhood-based method was proposed and widely used in the change detection of synthetic aperture radar (SAR) images because the neighborhood information of SAR images is effective to reduce the negative effect of speckle noise. Nevertheless, for the neighborhood-based method, it is unreasonable to use a fixed window size for the entire image because the optimal window size of different pixels in an image is different. Hence, if you let the neighborhood-based method use a large window to significantly suppress noise, it cannot preserve the detail information such as the edge of a changed area. To overcome this drawback, we propose a spatial-temporal adaptive neighborhood-based ratio (STANR) approach for change detection in SAR images. STANR employs heterogeneity to adaptively select the spatial homogeneity neighborhood and uses the temporal adaptive strategy to determine multi-temporal neighborhood windows. Experimental results on two data sets show that STANR can both suppress the negative influence of noise and preserve edge details, and can obtain a better difference image than other state-of-the-art methods.

Optimizing a Parameterized Plug-and-Play ADMM for Iterative Low-Dose CT Reconstruction.

Reducing the exposure to X-ray radiation while maintaining a clinically acceptable image quality is desirable in various CT applications. To realize low-dose CT (LdCT) imaging, model-based iterative reconstruction (MBIR) algorithms are widely adopted, but they require proper prior knowledge assumptions in the sinogram and/or image domains and involve tedious manual optimization of multiple parameters. In this paper, we propose a deep learning (DL)-based strategy for MBIR to simultaneously address prior knowledge design and MBIR parameter selection in one optimization framework. Specifically, a parameterized plug-and-play alternating direction method of multipliers (3pADMM) is proposed for the general penalized weighted least-squares model, and then, by adopting the basic idea of DL, the parameterized plug-and-play (3p) prior and the related parameters are optimized simultaneously in a single framework using a large number of training data. The main contribution of this paper is that the 3p prior and the related parameters in the proposed 3pADMM framework can be supervised and optimized simultaneously to achieve robust LdCT reconstruction performance. Experimental results obtained on clinical patient datasets demonstrate that the proposed method can achieve promising gains over existing algorithms for LdCT image reconstruction in terms of noise-induced artifact suppression and edge detail preservation.

A Robust Edge Detection Approach in the Presence of High Impulse Noise Intensity through Switching Adaptive Median and Fixed Weighted Mean Filtering.

This study introduces a robust edge detection method that relies on an integrated process for denoising images in the presence of high impulse noise. This process is shown to be resilient to impulse (or salt and pepper) noise even under high intensity levels. The proposed switching adaptive median and fixed weighted mean filter (SAMFWMF) is shown to yield optimal edge detection and edge detail preservation, an outcome we validate through high correlation, structural similarity index and peak signal to noise ratio measures. For comparative purposes, a comprehensive analysis of other denoising filters is provided based on these various validation metrics. The nonmaximum suppression method and new edge following maximum-sequence are two techniques used to track the edges and overcome edge discontinuities and noisy pixels, especially in the presence of high-intensity noise levels. After applying predefined thresholds to the grayscale image and thus obtaining a binary image, several morphological operations are used to remove the unwanted edges and noisy pixels, perform edge thinning to ultimately provide the desired edge connectivity which results in an optimal edge detection method. The obtained results are compared to other existing state-of-the-art denoising filters and other edge detection methods in support of our assertion that the proposed method is resilient to impulse noise even under high-intensity levels.

Coherent noise reduction of phase images in digital holographic microscopy based on the adaptive anisotropic diffusion.

The suppression of coherent noise can produce higher-quality reconstructed images in digital holographic microscopy. A robust and effective phase coherent noise denoising algorithm is proposed in this paper that combines the anisotropic diffusion equation and the phase quality map. In order to accurately identify the noise and signal pixels, we introduce the phase quality map and edge detection to quantify the quality of the pixel information. In addition, a synthetic diffusion function is established to control the speed of the anisotropic diffusion process based on the quality coefficient. Several experiments have been carried out to validate the effectiveness of the proposed algorithm for coherent noise reduction. The results demonstrate that the proposed algorithm can reduce coherent noise and preserve edge details well.