Resolution enhancement of Earth’s images from synthetic aperture radars (SARs), used for applications that require scene interpretations and detailed analysis, fails due to the presence of inherent speckle noise. An inexpensive alternative solution to the problem is to use super-resolution (SR) algorithms that deal with speckle. A novel approach to augment kernel regression into the Adaptive Importance Sampling Unscented Kalman Filter (AISUKF) framework for SAR image SR has been presented in this letter. We have experimented with three different nonlinear kernel regressions, namely, arc-cosine kernel, radial basis function kernel, and steering kernel (SK) regressions. Empirical results suggest that AISUKF with SK regression is more appropriate for the abovementioned SR problem resulting in a better denoised and more detail-preserved output.

Adaptive Importance Sampling Unscented Kalman Filter With Kernel Regression for SAR Image Super-Resolution

As an active microwave coherent imaging technology, synthetic aperture radar (SAR) images suffer from severe speckle noise and low-resolution problems due to the limitations of the imaging system, which cause difficulties in image interpretation and target detection. However, the existing SAR super-resolution (SR) methods usually reconstruct the images by a determined degradation model and hardly consider multiplicative speckle noise, meanwhile, most SR models are trained with synthetic datasets in which the low-resolution (LR) images are down-sampled from their high-resolution (HR) counterparts. These constraints cause a serious domain gap between the synthetic and real SAR images. To solve the above problems, this paper proposes an unsupervised blind SR method for SAR images by introducing SAR priors in a cycle-GAN framework. First, a learnable probabilistic degradation model combined with SAR noise priors was presented to satisfy various SAR images produced from different platforms. Then, a degradation model and a SR model in a unified cycle-GAN framework were trained simultaneously to learn the intrinsic relationship between HR–LR domains. The model was trained with real LR and HR SAR images instead of synthetic paired images to conquer the domain gap. Finally, experimental results on both synthetic and real SAR images demonstrated the high performance of the proposed method in terms of image quality and visual perception. Additionally, we found the proposed SR method demonstrates the tremendous potential for target detection tasks by reducing missed detection and false alarms significantly.

/pdf/blind-super-resolution-for-sar-images-with-speckle-noise-1h1e7js9.pdf

Blind Super-Resolution for SAR Images with Speckle Noise Based on Deep Learning Probabilistic Degradation Model and SAR Priors

This paper presents how this image super-resolution works in different areas in the real world. The technique of converting low-quality pictures into high-resolution images is referred to as Image Super Resolution. We can achieve this using different techniques. In this paper, we show how we can increase the quality of images using the SRCNN technique. We have given a brief introduction to the SRCNN technique. We have calculated the PSNR, MSE, and SSIM values for some images after using the SRCNN method. 

Digital Image Enhancement using Conventional Neural Network

SAR image enhancement plays an important role in the process of SAR image processing and information interpretation. Super-resolution reconstruction is a widely adopted enhancement method. However, it is difficult to achieve a decent tradeoff between reconstruction effectiveness and the convergence speed for existing methods. To combat such problem, this paper proposed a novel solution to it, we started from the modeling of SAR image degradation principle, applying the adaptive line search strategy to the SAR image super-resolution reconstruction process, and redefined the step size selection in the reconstruction process, made it possible to achieve both reconstruction effectiveness and convergence speed. Compared with the existing empirical setting or iterative selection, the proposed method can reduce the number of iterations while guarantee the reconstruction results.

SAR Image Super-Resolution Reconstruction Based on an Optimize Iterative Method for Regularization

Resolution enhancement of Earth’s images from synthetic aperture radars (SARs), used for applications that require scene interpretations and detailed analysis, fails due to the presence of inherent speckle noise. An inexpensive alternative solution to the problem is to use super-resolution (SR) algorithms that deal with speckle. A novel approach to augment kernel regression into the Adaptive Importance Sampling Unscented Kalman Filter (AISUKF) framework for SAR image SR has been presented in this letter. We have experimented with three different nonlinear kernel regressions, namely, arc-cosine kernel, radial basis function kernel, and steering kernel (SK) regressions. Empirical results suggest that AISUKF with SK regression is more appropriate for the abovementioned SR problem resulting in a better denoised and more detail-preserved output. 

As is well known that the quality of a digital image is affected by many factors, such as the distance between the acquisition system and the object, the acquisition environment conditions, and the resolution of the image sensors employed, and so on So how to obtain high quality images has become a difficult task in practical applications Image super-resolution reconstruction (SRR) is a signal processing approach which can produces a high-resolution (HR) image from observed multiple low-resolution (LR) images The purpose of this study is to evaluate the potential application of SR techniques to synthetic aperture radar (SAR) In this paper, a LR image is used to extract high frequency information and reconstruct a high resolution image by an interpolation-restoration method based on contourlet transform to solve the problem of SRR Experimental results show that the method has the superior performance in subjective visual quality

A contourlet-based interpolation restoration method for super-resolution of SAR image

Image segmentation is an important processing technology, which is the basis of image recognition and has been widely used in many fields. In this paper, we propose a method, termed coarse-to-fine strategy-based image segmentation (CSIS), for color image segmentation. The basic idea is to segment an image by three phases: (1) the original image is first segmented into several distinct regions by using the mean shift method; (2) the segmented regions are converted to a weighted region adjacency graph (RAG), and a new graph cut method, called multi-cut algorithm, is proposed to partition the RAG into multiple regions; (3) a one-step Chan–Vese algorithm is applied to smooth the boundaries of the segmented objectives. In each of the last two phases, a method is applied to refine the result obtained in the previous phase. By carefully balancing the efforts used in each phase, CSIS could segment color images both efficiently and effectively. These advantages are demonstrated by applying the proposed method to a variety of test instances, and the statistical results also show that it is comparable with some state-of-the-art methods.

Color Image Segmentation by Utilizing Coarse-to-Fine Strategy

BACKGROUND
In fan beam X-ray imaging applications, several X-ray images sometimes need to be stitched together into a panoramic image because of the size limitations of the detector.


OBJECTIVE
This study aims to propose a novel multi-view X-ray digital imaging stitching algorithm (MVS) based on the CdZnTe photon counting linear array detectors to solve the problem of fan beam X-ray stitching deformation.


METHODS
The panoramic image is generated in four steps including (1) multi-view projection data acquisition, (2) overlapping positioning, (3) weighted fusion and (4) projected pixel value calculation. Images of a globe and foot are scanned by fan beam X-rays and a CdZnTe detector. The proposed method is applied to stitch together the scanned images of the globe. Three other methods are also used for comparison. Finally, this MVS algorithm is also used in the stitching of scanned images of the foot.


RESULTS
Compared with the 50% stitching accuracy of other methods, the new MVS algorithm reached a stitching accuracy of 94.4% . Image distortion on the globe and feet is also eliminated and thus image quality is significantly improved.


CONCLUSIONS
This study proposes a new multi-view X-ray digital imaging stitching algorithm. Study results demonstrate the superiority of this new algorithm and its feasibility in practical applications.

A novel multi-view X-ray digital imaging stitching algorithm.

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A contourlet-based interpolation restoration method for super-resolution of SAR image

Color Image Segmentation by Utilizing Coarse-to-Fine Strategy

A novel multi-view X-ray digital imaging stitching algorithm.