SAR Image Denoising Based on Wavelet Packet and Median Filter

2013 ◽  
Vol 333-335 ◽  
pp. 916-919 ◽  
Author(s):  
Hui Huang Zhao ◽  
Juan F. Lopez Jr ◽  
Alex Martinez ◽  
Zhi Jun Qiao
Keyword(s):  
Image Processing ◽  
Image Denoising ◽  
Wavelet Packet ◽  
Median Filter ◽  
The Novel ◽  
Sar Image ◽  
Sar Images ◽  
Processing Approach ◽  
Filter Algorithms

In this study a novel image processing approach is proposed to improve the denoising in SAR images based on wavelet packet and median filter, Median filter is adopted to remove noise in the wavelet packet domain. At first, the process of the novel is introduced in detail .At last, by adding some simulated noise in the SAR image, the performance of the proposed approach is shown and compared with other filter algorithms in terms of PSNR

scholarly journals A New Synthetic Aperture Radar (SAR) Imaging Method Combining Match Filter Imaging and Image Edge Enhancement

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4133 ◽  
Author(s):  
Bing Sun ◽  
Chuying Fang ◽  
Hailun Xu ◽  
Anqi Gao
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Imaging Method ◽  
Sar Image ◽  
Sar Images ◽  
Gradient Operator ◽  
Sar Imaging ◽  
Aperture Radar

In general, synthetic aperture radar (SAR) imaging and image processing are two sequential steps in SAR image processing. Due to the large size of SAR images, most image processing algorithms require image segmentation before processing. However, the existence of speckle noise in SAR images, as well as poor contrast and the uneven distribution of gray values in the same target, make SAR images difficult to segment. In order to facilitate the subsequent processing of SAR images, this paper proposes a new method that combines the back-projection algorithm (BPA) and a first-order gradient operator to enhance the edges of SAR images to overcome image segmentation problems. For complex-valued signals, the gradient operator was applied directly to the imaging process. The experimental results of simulated images and real images validate our proposed method. For the simulated scene, the supervised image segmentation evaluation indexes of our method have more than 1.18%, 11.2% and 11.72% improvement on probabilistic Rand index (PRI), variability index (VI), and global consistency error (GCE). The proposed imaging method will make SAR image segmentation and related applications easier.

scholarly journals Implementation and Optimization of Image Processing on the Map of SABRE i.MX_6

2021 ◽  
pp. 402-417
Author(s):  
Waheed Muhammad SANYA ◽  
Gaurav BAJPAI ◽  
Haji Ali HAJI
Keyword(s):  
Image Processing ◽  
Image Denoising ◽  
Median Filter ◽  
Random Noise ◽  
Optimization Techniques ◽  
Image Process ◽  
Algorithm Optimization ◽  
Visual Appearance ◽  
Algorithmic Optimization ◽  
Different Response

Vision relieves humans to understand the environmental deviations over a period. These deviations are seen by capturing the images. The digital image plays a dynamic role in everyday life. One of the processes of optimizing the details of an image whilst removing the random noise is image denoising. It is a well-explored research topic in the field of image processing. In the past, the progress made in image denoising has advanced from the improved modeling of digital images. Hence, the major challenges of the image process denoising algorithm is to advance the visual appearance whilst preserving the other details of the real image. Significant research today focuses on wavelet-based denoising methods. This research paper presents a new approach to understand the Sobel imaging process algorithm on the Linux platform and develop an effective algorithm by using different optimization techniques on SABRE i.MX_6. Our work concentrated more on the image process algorithm optimization. By using the OpenCV environment, this paper is intended to simulate a Salt and Pepper noisy phenomenon and remove the noisy pixels by using Median Filter Algorithm. The Sobel convolution method included and used in the design of a Sobel Filter and then process the image following the median filter, to achieve an effective edge detection result. Finally, this paper optimizes the algorithm on SABRE i.MX_6 Linux environment. By using algorithmic optimization (lower complexity algorithm in the mathematical sense, using appropriate data structures), optimization for RISC (loop unrolling) processors, including optimization for efficient use of hardware resources (access to data, cache management and multi-thread), this paper analyzed the different response parameters of the system with varied inputs, different compiler options (O1, O2, or O3), and different doping degrees. The proposed denoising algorithm shows the meaningful addition of the visual quality of the images and the algorithmic optimization assessment.

Sar Image Denoising Based on Multifractal and Regularity Analysis

2012 ◽  
Vol 500 ◽  
pp. 534-539
Author(s):  
Yun Ping Chen ◽  
Yan Chen ◽  
Ling Tong
Keyword(s):  
Image Denoising ◽  
Electromagnetic Waves ◽  
Multifractal Spectrum ◽  
Speckle Noise ◽  
Structural Features ◽  
Sar Image ◽  
Hölder Exponent ◽  
Sar Images ◽  
Equivalent Number

Synthetic Aperture Radar (SAR) images are corrupted by multiplicative speckle noise, which is due to random interference of electromagnetic waves. The noise degrades the quality of images and makes them hard to be interpreted, analyzed and classified. It appears sensible to reduce speckle in SAR images, while the structural features and textural information are not lost. This paper applies the framework of multifractal and regularity analysis to SAR image enhancement and denoising. The method does not make explicit assumptions about the model of the noise, but rather supposes that image denoising is equivalent to increasing the Hölder exponent at each point. The image is characterized via its multifractal spectrum, which mode yields the most frequent Hölder exponent. This manipulation leads to a smooth image while preserving the useful information in the signal. In order to evaluate the restoration result, Equivalent Number of Look (ENL) and edge save index (ESI) are used as criterion. Better result is obtained when regularity increase equal 0.5 (δ=0.5).

scholarly journals Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1643
Author(s):  
Ming Liu ◽  
Shichao Chen ◽  
Fugang Lu ◽  
Mengdao Xing ◽  
Jingbiao Wei
Keyword(s):  
Synthetic Aperture Radar ◽  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Synthetic Aperture ◽  
False Alarms ◽  
Sar Image ◽  
Constant False Alarm Rate ◽  
Sar Images ◽  
Complex Scenes

For target detection in complex scenes of synthetic aperture radar (SAR) images, the false alarms in the land areas are hard to eliminate, especially for the ones near the coastline. Focusing on the problem, an algorithm based on the fusion of multiscale superpixel segmentations is proposed in this paper. Firstly, the SAR images are partitioned by using different scales of superpixel segmentation. For the superpixels in each scale, the land-sea segmentation is achieved by judging their statistical properties. Then, the land-sea segmentation results obtained in each scale are combined with the result of the constant false alarm rate (CFAR) detector to eliminate the false alarms located on the land areas of the SAR image. In the end, to enhance the robustness of the proposed algorithm, the detection results obtained in different scales are fused together to realize the final target detection. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.

scholarly journals Target Detection Network for SAR Images Based on Semi-Supervised Learning and Attention Mechanism

2021 ◽  
Vol 13 (14) ◽  
pp. 2686
Author(s):  
Di Wei ◽  
Yuang Du ◽  
Lan Du ◽  
Lu Li
Keyword(s):  
Feature Extraction ◽  
Supervised Learning ◽  
Target Detection ◽  
Scene Recognition ◽  
Attention Mechanism ◽  
Sar Image ◽  
Target Level ◽  
Sar Images ◽  
Training Samples ◽  
Pass Through

The existing Synthetic Aperture Radar (SAR) image target detection methods based on convolutional neural networks (CNNs) have achieved remarkable performance, but these methods require a large number of target-level labeled training samples to train the network. Moreover, some clutter is very similar to targets in SAR images with complex scenes, making the target detection task very difficult. Therefore, a SAR target detection network based on a semi-supervised learning and attention mechanism is proposed in this paper. Since the image-level label simply marks whether the image contains the target of interest or not, which is easier to be labeled than the target-level label, the proposed method uses a small number of target-level labeled training samples and a large number of image-level labeled training samples to train the network with a semi-supervised learning algorithm. The proposed network consists of a detection branch and a scene recognition branch with a feature extraction module and an attention module shared between these two branches. The feature extraction module can extract the deep features of the input SAR images, and the attention module can guide the network to focus on the target of interest while suppressing the clutter. During the semi-supervised learning process, the target-level labeled training samples will pass through the detection branch, while the image-level labeled training samples will pass through the scene recognition branch. During the test process, considering the help of global scene information in SAR images for detection, a novel coarse-to-fine detection procedure is proposed. After the coarse scene recognition determining whether the input SAR image contains the target of interest or not, the fine target detection is performed on the image that may contain the target. The experimental results based on the measured SAR dataset demonstrate that the proposed method can achieve better performance than the existing methods.

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