scholarly journals A New Biomedical Image Denoising Method Using an Adaptive Multi-resolution Technique

2022 ◽  
Vol 17 ◽  
pp. 16-24
Author(s):  
Lalit Mohan Satapathy ◽  
Pranati Das
Keyword(s):  
Image Processing ◽  
Image Denoising ◽  
State Of The Art ◽  
Vital Role ◽  
Primary Objective ◽  
Simple Task ◽  
Denoising Method ◽  
Biomedical Image ◽  
Mode Decomposition ◽  
Practical Challenge

In the world of digital image processing, image denoising plays a vital role, where the primary objective was to distinguish between a clean and a noisy image. However, it was not a simple task. As a consequence of everyone's understanding of the practical challenge, a variety of methods have been presented during the last few years. Of those, wavelet transformer-based approaches were the most common. But wavelet-based methods have their own limitations in image processing applications like shift sensitivity, poor directionality, and lack of phase information, and they also face difficulties in defining the threshold parameters. As a result, this study provides an image de-noising approach based on Bi-dimensional Empirical Mode Decomposition (BEMD). This project's main purpose is to disintegrate noisy images based on their frequency and construct a hybrid algorithm that uses existing de-noising techniques. This approach decomposes the noisy picture into numerous IMFs with residue, which were subsequently filtered independently based on their specific properties. To quantify the success of the proposed technique, a comprehensive analysis of the experimental results of the benchmark test images was conducted using several performance measurement matrices. The reconstructed image was found to be more accurate and pleasant to the eye, outperforming state-of-the-art denoising approaches in terms of PSNR, MSE, and SSIM.

Comparison and Improvements of Image Denoising Based on Wavelet Transform

2015 ◽  
Vol 740 ◽  
pp. 644-647
Author(s):  
Xue Mei Xiao
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Image Denoising ◽  
Wavelet Denoising ◽  
Important Application ◽  
Denoising Method ◽  
Advantages And Disadvantages ◽  
Wavelet Transform Modulus Maxima ◽  
Modulus Maxima

Wavelet transform denoising is an important application of wavelet analysis in signal and image processing. Several popular wavelet denoising methods are introduced including the Mallat forced denoising, the wavelet transform modulus maxima method and the nonlinear wavelet threshold denoising method. Their advantages and disadvantages are compared, which may be helpful in selecting the wavelet denoising methods. At the same time, several improvement methods are offered.

scholarly journals Research on Image Denoising and Super-Resolution Reconstruction Technology of Multiscale-Fusion Images

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Size Li ◽  
Pengjiang Qian ◽  
Xin Zhang ◽  
Aiguo Chen
Keyword(s):  
Image Processing ◽  
Receptive Field ◽  
Image Denoising ◽  
State Of The Art ◽  
Experimental Studies ◽  
Super Resolution ◽  
Image Resolution ◽  
Reconstruction Method ◽  
Model Learning ◽  
Image Super Resolution

Image denoising and image super-resolution reconstruction are two important techniques for image processing. Deep learning is used to solve the problem of image denoising and super-resolution reconstruction in recent years, and it usually has better results than traditional methods. However, image denoising and super-resolution reconstruction are studied separately by state-of-the-art work. To optimally improve the image resolution, it is necessary to investigate how to integrate these two techniques. In this paper, based on Generative Adversarial Network (GAN), we propose a novel image denoising and super-resolution reconstruction method, i.e., multiscale-fusion GAN (MFGAN), to restore the images interfered by noises. Our contributions reflect in the following three aspects: (1) the combination of image denoising and image super-resolution reconstruction simplifies the process of upsampling and downsampling images during the model learning, avoiding repeated input and output images operations, and improves the efficiency of image processing. (2) Motivated by the Inception structure and introducing a multiscale-fusion strategy, our method is capable of using the multiple convolution kernels with different sizes to expand the receptive field in parallel. (3) The ablation experiments verify the effectiveness of each employed loss measurement in our devised loss function. And our experimental studies demonstrate that the proposed model can effectively expand the receptive field and thus reconstruct images with high resolution and accuracy and that the proposed MFGAN method performs better than a few state-of-the-art methods.

scholarly journals Iterative Regularization Denoising Method Based on OSV Model for BioMedical Image Denoising

2011 ◽  
Vol 277 ◽  
pp. 012005
Author(s):  
Chen Guan-nan ◽  
Chen Rong ◽  
Huang Zu-fang ◽  
Lin Ju-qiang ◽  
Feng Shang-yuan ◽  
...  
Keyword(s):  
Image Denoising ◽  
Iterative Regularization ◽  
Denoising Method ◽  
Biomedical Image

scholarly journals A Novel Strategy to Image Decomposition for Wireless Capsule Endoscopy Images

2020 ◽  
Vol 9 (4) ◽  
pp. 535-539
Keyword(s):  
Image Denoising ◽  
Capsule Endoscopy ◽  
Image Decomposition ◽  
Wireless Capsule Endoscopy ◽  
Denoising Method ◽  
Research Article ◽  
Mode Decomposition ◽  
Novel Strategy ◽  
Bidimensional Empirical Mode Decomposition ◽  
Wireless Capsule

Now a day wireless capsule endoscopy (WCE) is broadly used for detection of gastro internal organ diseases. WCE is produces quite 55000 images but still there is challenging task of it that captured noisy images. Removing noise from images is difficult aspiration for image denoising technique. Therefore, various redundant blur and amounts of remaining noise ought to be analysis to research the particular results of denoising method. In this research article, different methods are used for image denoising and evaluated performance for wireless capsule endoscopy images. The proposed approach is suggested Bidimensional Empirical Mode Decomposition (BEMD) for WCE images. Here evaluate performance of BEMD method and wavelet. Computer simulation proved that proposed technique offer considerable advantage than other method.

scholarly journals Fully Symmetric Convolutional Network for Effective Image Denoising

2019 ◽  
Vol 9 (4) ◽  
pp. 778 ◽  
Author(s):  
Steffi Priyanka ◽  
Yuan-Kai Wang
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Image Denoising ◽  
Gpu Computing ◽  
State Of The Art ◽  
Large Data ◽  
Convolutional Network ◽  
Proposed Model ◽  
Feature Extractor ◽  
A Chain

Neural-network-based image denoising is one of the promising approaches to deal with problems in image processing. In this work, a deep fully symmetric convolutional–deconvolutional neural network (FSCN) is proposed for image denoising. The proposed model comprises a novel architecture with a chain of successive symmetric convolutional–deconvolutional layers. This framework learns convolutional–deconvolutional mappings from corrupted images to the clean ones in an end-to-end fashion without using image priors. The convolutional layers act as feature extractor to encode primary components of the image contents while eliminating corruptions, and the deconvolutional layers then decode the image abstractions to recover the image content details. An adaptive moment optimizer is used to minimize the reconstruction loss as it is appropriate for large data and noisy images. Extensive experiments were conducted for image denoising to evaluate the FSCN model against the existing state-of-the-art denoising algorithms. The results show that the proposed model achieves superior denoising, both qualitatively and quantitatively. This work also presents the efficient implementation of the FSCN model by using GPU computing which makes it easy and attractive for practical denoising applications.

scholarly journals An Adaptive Weighted Image Denoising Method Based on Morphology

2021 ◽  
Vol 15 ◽  
pp. 271-279
Author(s):  
Jinjuan Wang ◽  
Shan Duan ◽  
Qun Zhou
Keyword(s):  
Image Processing ◽  
Image Denoising ◽  
Mathematical Morphology ◽  
Edge Preservation ◽  
Denoising Method ◽  
Edge Information ◽  
Processing Step ◽  
Blurred Image ◽  
Image Edge ◽  
Cascaded Filter

In its generation, transmission and record, image signal is often interfered by various noises, which have severally affected the visual effects of images; therefore, it is a very important pre-processing step to take proper approaches to reduce noises. Conventional denoising methods have also blurred image edge information while removing noises, which can be overcome by the method based on mathematical morphology. While eliminating different noises from images, it can not only keep clear object edges, but also preserve as many image details as possible and it also has excellent capacities in noise resistance and edge preservation. With image denoising and mathematical morphology as the research subject, this paper analyzes the generation and characteristics of common image noises, studies the basic theories of mathematical morphology and its applications in image processing, discusses the method to select structural elements in mathematical morphology and proposes a filtering algorithm which combines image denoising and mathematical morphology. This method conducts morphological filtering and denoising on noised image with filter cascade and its performance is verified with stimulation testing. The experiment results prove that the approach to build the morphological filter into cascaded filter through series and parallel connection can to a certain extent, affect the effect of common filter while being applied to different image processing.

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