scholarly journals Despeckling Algorithms For Removing Noise In Medical Images

2021 ◽  
Vol 12 (6) ◽  
pp. 87-94
Author(s):  
D.Devasena Et.al
Keyword(s):  
Synthetic Aperture Radar ◽  
Satellite Images ◽  
Medical Images ◽  
Imaging Techniques ◽  
Speckle Noise ◽  
Synthetic Aperture ◽  
Subjective Analysis ◽  
Radar Images ◽  
Different Types ◽  
Aperture Radar

The medical and satellite images are mostly corrupted by a multiplicative granular noise called speckle noise which degrades the quality of the images captured by using medical imaging techniques and also Synthetic Aperture Radar images. It causes difficulties in image interpretation and this is mainly due to back scattered signals from the multiple targets. In medical field, the diagnosis of the tissues, bones and organs takes place by using imaging techniques. By using different imaging techniques, the medical images are captured and used for diagnosis. Different types of filtering techniques are proposed in the literature to remove the speckle noise in medical and satellite images. In this research paper different types of adaptive filters and its modifications are proposed and compared. The filters like modified lee filter, modified Edge Enhanced lee filter, modified fast bilateral filter and Modified Particle Swarm Optimization based despeckling algorithm. The results are verified for both simulated images and real medical images and also for Synthetic Aperture Radar images. The results are compared in terms of both objective and subjective analysis for simulated and real medical images. The simulation is done using MATLAB R2013 and the visual qualities of the images are analyzed for varying noise densities.

scholarly journals Removing Speckle Noise in Synthetic Aperture Radar Images using Linear Filters

2020 ◽  
Vol 9 (3S) ◽  
pp. 395-397
Keyword(s):  
Synthetic Aperture Radar ◽  
Speckle Noise ◽  
Synthetic Aperture ◽  
Linear Filtering ◽  
Linear Filters ◽  
Radar Images ◽  
Hybrid Filtering ◽  
Hybrid Filters ◽  
Aperture Radar

Speckle is a granular aggravation, typically demonstrated as a multiplicative noise that influences Synthetic aperture radar (SAR) pictures, just as every single cognizant picture. In the course of the most recent three decades, a few techniques have been proposed for the decrease of spot, or despeckling, in SAR pictures. The examination begins with the linear filtering, non-linear filtering, adaptive filtering, and hybrid filtering. In spite of the fact that the old style straight separating strategies have lower execution similarly, the hybridization between them beats than the as of late proposed techniques. Be that as it may, the cautious determination of such filters and their impacting request exceptionally influences the presentation of such filtering methodologies. In this paper, Hybrid filtering is proposed for SAR despeckling, which involves the improved variants of frost, median and mean filters. The presentation of the proposed framework is broke down and contrasted and the as of late SAR despeckling strategies. The outcomes demonstrate that the hybrid filters are focused as they can despeckle the SAR pictures superior to the current methods

scholarly journals Self-Supervised Despeckling Algorithm with an Enhanced U-Net for Synthetic Aperture Radar Images

2021 ◽  
Vol 13 (21) ◽  
pp. 4383
Author(s):  
Gang Zhang ◽  
Zhi Li ◽  
Xuewei Li ◽  
Sitong Liu
Keyword(s):  
Synthetic Aperture Radar ◽  
Real World ◽  
Speckle Noise ◽  
Synthetic Aperture ◽  
Noisy Image ◽  
Deep Convolutional Neural Networks ◽  
Sar Images ◽  
Radar Images ◽  
Image Pairs ◽  
Aperture Radar

Self-supervised method has proven to be a suitable approach for despeckling on synthetic aperture radar (SAR) images. However, most self-supervised despeckling methods are trained by noisy-noisy image pairs, which are constructed by using natural images with simulated speckle noise, time-series real-world SAR images or generative adversarial network, limiting the practicability of these methods in real-world SAR images. Therefore, in this paper, a novel self-supervised despeckling algorithm with an enhanced U-Net is proposed for real-world SAR images. Firstly, unlike previous self-supervised despeckling works, the noisy-noisy image pairs are generated from real-word SAR images through a novel generation training pairs module, which makes it possible to train deep convolutional neural networks using real-world SAR images. Secondly, an enhanced U-Net is designed to improve the feature extraction and fusion capabilities of the network. Thirdly, a self-supervised training loss function with a regularization loss is proposed to address the difference of target pixel values between neighbors on the original SAR images. Finally, visual and quantitative experiments on simulated and real-world SAR images show that the proposed algorithm notably removes speckle noise with better preserving features, which exceed several state-of-the-art despeckling methods.

scholarly journals Ship Detection Based on Information Theory and Segmentation from Synthetic Aperture Radar (SAR) Images

2020 ◽  
Vol 8 (6) ◽  
pp. 2513-2517
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Satellite Images ◽  
Synthetic Aperture ◽  
Morphological Operations ◽  
Sar Images ◽  
Ship Detection ◽  
Radar Images ◽  
Vessel Detection ◽  
Aperture Radar

Ship detection is a procedure which asserts in fields such as ocean and sea management, vessel detection, marine superintendence, and rein, and also can be applied to exclude extralegal actions. Remote sensing can be utilized as a potential tool for zonular and universal monitoring to attain the forenamed goals. Among the radar images, the precious datum from Synthetic Aperture Radar (SAR) is playing a serious duty in remote sensing. Howsoever, vessel detecting in heterogeneous and strong clutter is still a question in this regard. The letter points to a ship detection scheme for SAR images exploiting a segmentation-based morphological operation using entropy. In the presented scheme, the morphological operations are adopted to intercept the background and foreground in the satellite images. The method was implemented and tested on the homogenous, heterogeneous and strong clutter SAR images and the results are promising and showing that the proposed method can improve the vessel detection from homogenous and heterogeneous and strong clutter satellite images

scholarly journals Removing Speckle Noise in Synthetic Aperture Radar Images using Combined Intensity Coherence Vector & Hybrid Filtering

2021 ◽  
Vol 58 (1) ◽  
pp. 4289-4295
Author(s):  
Dr. D. Suresh Et al.
Keyword(s):  
Synthetic Aperture Radar ◽  
Speckle Noise ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Signal To Noise ◽  
Sar Images ◽  
Radar Images ◽  
Hybrid Filtering ◽  
Edge Based ◽  
Aperture Radar

Noise will be unavoidable in image securing practice and denoising is a fundamental advance to recoup the image quality. The image of Synthetic Aperture Radar (SAR) is intrinsically misrepresented in dot noise that happens because of coherent nature of the dispersing wonders. Denoising SAR images target eliminating dot while safeguarding image highlights, for example, surface, edges, and point targets. The blend of nonlocal gathering and changed area filtering has coordinated the cutting edge denoising methods. Notwithstanding, this methodology makes an intense suspicion that image fix itself gives a brilliant guess on the genuine boundary, which prompts predisposition issue transcendently under genuine dot noise. Another impediment is that the for the most part utilized fix pre-determination techniques can't productively avoid the exceptions and harm the edges. The SAR image is infused with spot noise, and afterward edge based marker controlled watershed division is applied to recognize the homogeneous locales in SAR image. For every locale, the local pixels are distinguished by utilizing Intensity Coherence Vector (ICV) and are denoised autonomously by utilizing a half and half filtering, which contains the improved forms of ice, middle and mean channel. The exploratory outcomes show that the proposed strategy outflanks different techniques, for example, fix based filtering, non-nearby methods, wavelets and old style dot channels in wording higher wavelets signal-to-noise and edge conservation proportions relatively.

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