scholarly journals Weighted lp Norm Sparse Error Constraint Based ADMM for Image Denoising

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Jiucheng Xu ◽  
Nan Wang ◽  
Zhanwei Xu ◽  
Keqiang Xu
Keyword(s):  
Image Denoising ◽  
Color Image ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Lp Norm ◽  
Alternating Direction ◽  
Proposed Model ◽  
Feature Similarity Index ◽  
Constraint Problem ◽  
Error Constraint

In the process of image denoising, the accurate prior knowledge cannot be learned due to the influence of noise. Therefore, it is difficult to obtain better sparse coefficients. Based on this consideration, a weighted lp norm sparse error constraint (WPNSEC) model is proposed. Firstly, the suitable setting of power p in the lp norm is made a detailed analysis. Secondly, the proposed model is extended to color image denoising. Since the noise of RGB channels has different intensities, a weight matrix is introduced to measure the noise levels of different channels, and a multichannel weighted lp norm sparse error constraint algorithm is proposed. Thirdly, in order to ensure that the proposed algorithm is tractable, the multichannel WPNSEC model is converted into an equality constraint problem solved via alternating direction method of multipliers (ADMM) algorithm. Experimental results on gray image and color image datasets show that the proposed algorithms not only have higher peak signal-to-noise ratio (PSNR) and feature similarity index (FSIM) but also produce better visual quality than competing image denoising algorithms.

scholarly journals Research on the Image Denoising Method Based on Partial Differential Equations

2016 ◽  
Vol 16 (5) ◽  
pp. 109-118
Author(s):  
Xiaolu Xie
Keyword(s):  
Image Denoising ◽  
High Performance ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Structural Similarity Index ◽  
Denoising Method ◽  
New Approach ◽  
Proposed Model ◽  
Index Measure

Abstract In this paper we propose a new approach for image denoising based on the combination of PM model, isotropic diffusion model, and TV model. To emphasize the superiority of the proposed model, we have used the Structural Similarity Index Measure (SSIM) and Peak Signal to Noise Ratio (PSNR) as the subjective criterion. Numerical experiments with different images show that our algorithm has the highest PSNR and SS1M, as well as the best visual quality among the six algorithms. Experimental results confirm the high performance of the proposed model compared with some well-known algorithms. In a word, the new model outperforms the mentioned three well known algorithms in reducing the Gibbs-type artifacts, edges blurring, and the block effect, simultaneously.

scholarly journals Application of Douglass-Gunn ADI Scheme on Diffusion Model with Different Noise Level for Image Denoising

2018 ◽  
Vol 7 (4.33) ◽  
pp. 10
Author(s):  
Nur Aimi Abdul Aziz ◽  
Suhaila Abd Halim
Keyword(s):  
Image Denoising ◽  
Noise Level ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Alternating Direction ◽  
Adi Scheme ◽  
Blurring Effect ◽  
Image Quality Index ◽  
Image Detail

Noise level is the amount of noise that corrupted the clear image in order to test on the filtering method of an algorithm proposed for image denoising. Most of the existing filtering techniques are able to remove noise but unable to preserve the image detail well and hence causing the blurring effect. Due to that, the objectives of this paper are to propose and implement Douglas-Gunn Alternating Direction Implicit (DG-ADI) on Anisotropic Diffusion (AD) model. Then, measure the performance of the proposed scheme with different level of noise. PDE based model is applied with the unconditional stable of DG-ADI scheme to remove the noise that corrupted the images. The AD model is used for preserving the image structures and edges. In this paper, a set of grayscale images from standard database is being filtered with three different noise levels in order to measure the performance of the proposed schemes. The performance of the proposed scheme is measured using the Mean Structural Similarity Index (MSSIM), Peak Signal to Noise Ratio (PSNR), Universal Image Quality Index (UIQI) and processing time. The implementation of the algorithm is completed using MATLAB R2013a. Experimental results show that the DG-ADI scheme able to remove noise with different noise level. The used of DG-ADI scheme in solving the AD model can remove the noise well without destroy the structure of image with appropriate parameters setting in grayscale image.  

scholarly journals A Multi-Scale Feature Extraction-Based Normalized Attention Neural Network for Image Denoising

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 319
Author(s):  
Yi Wang ◽  
Xiao Song ◽  
Guanghong Gong ◽  
Ni Li
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Image Denoising ◽  
Color Image ◽  
Rapid Development ◽  
Similarity Index ◽  
Structural Similarity ◽  
Convolutional Network ◽  
Scale Feature ◽  
Multi Scale

Due to the rapid development of deep learning and artificial intelligence techniques, denoising via neural networks has drawn great attention due to their flexibility and excellent performances. However, for most convolutional network denoising methods, the convolution kernel is only one layer deep, and features of distinct scales are neglected. Moreover, in the convolution operation, all channels are treated equally; the relationships of channels are not considered. In this paper, we propose a multi-scale feature extraction-based normalized attention neural network (MFENANN) for image denoising. In MFENANN, we define a multi-scale feature extraction block to extract and combine features at distinct scales of the noisy image. In addition, we propose a normalized attention network (NAN) to learn the relationships between channels, which smooths the optimization landscape and speeds up the convergence process for training an attention model. Moreover, we introduce the NAN to convolutional network denoising, in which each channel gets gain; channels can play different roles in the subsequent convolution. To testify the effectiveness of the proposed MFENANN, we used both grayscale and color image sets whose noise levels ranged from 0 to 75 to do the experiments. The experimental results show that compared with some state-of-the-art denoising methods, the restored images of MFENANN have larger peak signal-to-noise ratios (PSNR) and structural similarity index measure (SSIM) values and get better overall appearance.

scholarly journals Image Denoising in Spatial and Transform Domains

2021 ◽  
Author(s):  
Mina Sharifymoghaddam
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Weighting Scheme ◽  
Block Matching ◽  
Hard Thresholding ◽  
Weighted Version ◽  
Nonlocal Means ◽  
Processing Step ◽  
Index Measure

Image denoising is an inseparable pre-processing step of many image processing algorithms. Two mostly used image denoising algorithms are Nonlocal Means (NLM) and Block Matching and 3D Transform Domain Collaborative Filtering (BM3D). While BM3D outperforms NLM on variety of natural images, NLM is usually preferred when the algorithm complexity is an issue. In this thesis, we suggest modified version of these two methods that improve the performance of the original approaches. The conventional NLM uses weighted version of all patches in a search neighbourhood to denoise the center patch. However, it can include some dissimilar patches. Our first contribution, denoted by Similarity Validation Based Nonlocal Means (NLM-SVB), eliminates some of those unnecessary dissimilar patches in order to improve the performance of the algorithm. We propose a hard thresholding pre-processing step based on the exact distribution of distances of similar patches. Consequently, our method eliminates about 60% of dissimilar patches and improves NLM in terms of Peak Signal to Noise Ratio (PSNR) and Stracuteral Similarity Index Measure (SSIM). Our second contribution, denoted by Probabilistic Weighting BM3D (PW-BM3D), is the result of our thorough study of BM3D. BM3D consists of two main steps. One is finding a basic estimate of the noiseless image by hard thresholding coefficients. The second one is using this estimate to perform wiener filtering. In both steps the weighting scheme in the aggregation process plays an important role. The current weighting process depends on the variance of retrieved coefficients after denoising which results in a biased weighting. In PW-BM3D, we propose a novel probabilistic weighting scheme which is a function of the probability of similarity of noiseless patches in each 3D group. The results show improvement over BM3D in terms of PSNR for an average of about 0.2dB.

Symbiotic Organisms Search Optimization for Multilevel Image Thresholding

2020 ◽  
Vol 11 (2) ◽  
pp. 31-61
Author(s):  
Falguni Chakraborty ◽  
Provas Kumar Roy ◽  
Debashis Nandi
Keyword(s):  
Image Segmentation ◽  
Heuristic Algorithms ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Symbiotic Relationship ◽  
Symbiotic Organisms Search ◽  
Image Thresholding ◽  
Prime Concern ◽  
Feature Similarity Index ◽  
Symbiotic Organisms

Determination of optimum thresholds is the prime concern of any multilevel image thresholding technique. The traditional methods for multilevel thresholding are computationally expensive, time-consuming, and also suffer from lack of accuracy and stability. To address this issue, the authors propose a new methodology for multilevel image thresholding based on a recently developed meta-heuristic algorithm, Symbiotic Organisms Search (SOS). The SOS algorithm has been inspired by the symbiotic relationship among the organism in nature. This article has utilized the concept of the symbiotic relationship among the organisms to optimize three objective functions: Otsu's between class variance and Kapur's and Tsallis entropy for image segmentation. The performance of the SOS based image segmentation algorithm has been evaluated using a set of benchmark images and has been compared with four recent meta-heuristic algorithms. The algorithms are compared in terms of effectiveness and consistency. The quality of the algorithms has been estimated by some well-defined quality metrics such as peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), and, feature similarity index (FSIM). The experimental results of the algorithms reveal that the balance of intensification and diversification of the SOS algorithm to achieve the global optima is better than others.

Elephant Herding Optimization for Multi-Level Image Thresholding

2020 ◽  
Vol 11 (4) ◽  
pp. 64-90
Author(s):  
Falguni Chakraborty ◽  
Provas Kumar Roy ◽  
Debashis Nandi
Keyword(s):  
Image Segmentation ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Cuckoo Search ◽  
Threshold Value ◽  
Objective Functions ◽  
Image Thresholding ◽  
Bee Colony ◽  
Kapur’S Entropy ◽  
Feature Similarity Index

Multilevel thresholding plays a significant role in the arena of image segmentation. The main issue of multilevel image thresholding is to select the optimal combination of threshold value at different level. However, this problem has become challenging with the higher number of levels, because computational complexity is increased exponentially as the increase of number of threshold. To address this problem, this paper has proposed elephant herding optimization (EHO) based multilevel image thresholding technique for image segmentation. The EHO method has been inspired by the herding behaviour of elephant group in nature. Two well-known objective functions such as ‘Kapur's entropy' and ‘between-class variance method' have been used to determine the optimized threshold values for segmentation of different objects from an image. The performance of the proposed algorithm has been verified using a set of different test images taken from a well-known benchmark dataset named Berkeley Segmentation Dataset (BSDS). For comparative analysis, the results have been compared with three popular algorithms, e.g. cuckoo search (CS), artificial bee colony (ABC) and particle swarm optimization (PSO). It has been observed that the performance of the proposed EHO based image segmentation technique is efficient and promising with respect to the others in terms of the values of optimized thresholds, objective functions, peak signal-to-noise ratio (PSNR), structure similarity index (SSIM) and feature similarity index (FSIM). The algorithm also shows better convergence profile than the other methods discussed.

A compensating total variation image denoising model combining L1 and L2 norm

2020 ◽  
pp. 002072092092330
Author(s):  
Liqiong Zhang ◽  
Min Li ◽  
Xiaohua Qiu
Keyword(s):  
Image Denoising ◽  
Total Variation ◽  
Structural Information ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Isotropic Diffusion ◽  
Model Combining ◽  
L2 Norm ◽  
Staircase Effect

To overcome the “staircase effect” while preserving the structural information such as image edges and textures quickly and effectively, we propose a compensating total variation image denoising model combining L1 and L2 norm. A new compensating regular term is designed, which can perform anisotropic and isotropic diffusion in image denoising, thus making up for insufficient diffusion in the total variation model. The algorithm first uses local standard deviation to distinguish neighborhood types. Then, the anisotropic diffusion based on L1 norm plays the role of edge protection in the strong edge region. The anisotropic and the isotropic diffusion simultaneously exist in the smooth region, so that the weak textures can be protected while overcoming the “staircase effect” effectively. The simulation experiments show that this method can effectively improve the peak signal-to-noise ratio and obtain the higher structural similarity index and the shorter running time.

scholarly journals Color Image Generation from Range and Reflection Data of LiDAR

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5414
Author(s):  
Hyun-Koo Kim ◽  
Kook-Yeol Yoo ◽  
Ho-Youl Jung
Keyword(s):  
Color Image ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Point Clouds ◽  
Range Data ◽  
Fusion Method ◽  
Image Generation ◽  
Learning Networks ◽  
Reflection Data

Recently, it has been reported that a camera-captured-like color image can be generated from the reflection data of 3D light detection and ranging (LiDAR). In this paper, we present that the color image can also be generated from the range data of LiDAR. We propose deep learning networks that generate color images by fusing reflection and range data from LiDAR point clouds. In the proposed networks, the two datasets are fused in three ways—early, mid, and last fusion techniques. The baseline network is the encoder-decoder structured fully convolution network (ED-FCN). The image generation performances were evaluated according to source types, including reflection data-only, range data-only, and fusion of the two datasets. The well-known KITTI evaluation data were used for training and verification. The simulation results showed that the proposed last fusion method yields improvements of 0.53 dB, 0.49 dB, and 0.02 in gray-scale peak signal-to-noise ratio (PSNR), color-scale PSNR, and structural similarity index measure (SSIM), respectively, over the conventional reflection-based ED-FCN. Besides, the last fusion method can be applied to real-time applications with an average processing time of 13.56 ms per frame. The methodology presented in this paper would be a powerful tool for generating data from two or more heterogeneous sources.

scholarly journals New model for local fractional integral of Chebyshev polynomials for image denoising

2019 ◽  
pp. 22-28
Author(s):  
Suzan J Obaiys ◽  
Hamid A Jalab ◽  
Rabha W Ibrahim
Keyword(s):  
Image Denoising ◽  
Chebyshev Polynomials ◽  
Fractional Integral ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Input Image ◽  
Convolution Product ◽  
Local Fractional Integral ◽  
Image Pixels

The use of local fractional calculus has increased in different applications of image processing. This study proposes a new algorithm for image denoising to remove Gaussian noise in digital images. The proposed algorithm is based on local fractional integral of Chebyshev polynomials. The proposed structures of the local fractional windows are obtained by four masks created for x and y directions. On four directions, a convolution product of the input image pixels with the local fractional mask window has been performed. The visual perception and peak signal-to-noise ratio (PSNR) with the structural similarity index (SSIM) are used as image quality measurements. The experiments proved that the accomplished filtering results are better than the Gaussian filter. Keywords: local fractional; Chebyshev polynomials; Image denoising

scholarly journals Object Removal and Inpainting Technique by Lattice Boltzmann Method and Exemplar Method in Color Image

2019 ◽  
Vol 8 (6) ◽  
pp. 2008-2011
Keyword(s):  
Lattice Boltzmann Method ◽  
Lattice Boltzmann ◽  
Color Image ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Image Inpainting ◽  
Self Similarity ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Boltzmann Method

In this paper we use image inpainting technique in a color image. Inpainting is a technique in which, a missing area or pixels are replaced by adequate neighbouring pixels so that the resultant image obtained will look as no defect is there. In this paper we have removed an object from the color image and the vacant pixels are filled with the help of the surrounding neighbouring pixels. Two techniques are used here for removing and inpainting an object from the still color image, lattice boltzmann and exemplar method. These techniques were selected as Exemplar method of inpainting was used in many algorithms and have given a good result and lattice Boltzmann was usually used in experiments conducted in fluid dynamics. As motion of pixels can be compared to motion of fluids, lattice boltzmann method gives a better match for filling in the regions. Different variables for the inpainted image was calculated for both the methods. The signal to noise ratio and self-similarity index of the image is calculated for both inpainting techniques. According to the signal to noise ratio and the time taken for inpainting, it is found that lattice Boltzmann method of inpainting gives a better inpainted result..

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