International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 06 | June 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 3937 DESPECKLING OF SAR IMAGE USING CURVELET TRANSFORM Shraddha Mhaske 1 , Muzffarali Sayyad 2 1 PG Student (Digital Systems) & Sanjivani College of Engineering Kopargaon 2 Professor & Department of Electronics & Telecommunication Engineering, Sanjivani College of Engineering Kopargaon, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Researchers are striving hard to get rid of noise from images. Medical images and satellite images contain immense noise during capturing and transmission process. Therefore, the reduction of noise is a challenging task for researchers. Several methods have been developed in the past to reduce noise from images. Synthetic Aperture RADAR (SAR) images are generally affected by speckle noise or granular noise, during transmission. The present paper discloses the speckle noise reduction method using Curvelet transform. The Curvelet transform is more effective on the images to restore the edges of the images. The SAR image is used as input image and analysis is carried that shows better performance parameters like Peak Signal-to-Noise ratio (PSNR) and Mean Square Error (MSE). Key Words: Curvelet transform, image enhancement, SAR images, speckle noise reduction 1. INTRODUCTION Last decades, interest has grown among the researchers to capture, store, transmit and analyze image data. Image capturing is one of the effective ways to get data regarding an object, place, condition, etc. Sensing of images remotely is one of the efficient ways to get updates regarding the evolution of the natural phenomenon. This is one of the main reasons behind the increasing interest for remote sensing products in many fields of application, from homeland security to environmental protection or land resource management, just to quote some. The relevant among remote sensors for data acquisition are the Synthetic Aperture Radars (SAR). It is an active coherent sensor which stimulates the scene of interest through electromagnetic waves reproducing it by recording the backscattered signal; such a signal is thus managed by the SAR system in the image processing, in order to obtain the image. The interesting feature of the SAR compared with other sensors such as the optical, one is its microwave nature. This property offers the advantage of working with all weather and illumination conditions [1]. Moreover, varying the working frequency and so the penetration depth of the electromagnetic radiation, the information recorded is about the Earth surface, subsoil, hidden objects. Although SAR images are a powerful tool, their interpretation is not so easy: in fact, SAR images are affected by a strong noise called speckle, which degrades the performance of many image processing tasks, such as image segmentation, target detection, and classification, or recognition of regions of interest by expert human photo interpreter. In image processing, removing noise from the original image is still a challenging research. Several approaches have been introduced and each has its own assumption, advantages and disadvantages. The speckle noise is commonly found in the ultrasound medical and SAR images. Various techniques such as Adaptive filter, Partial Differential Equations based filters, Transform domain filters, Non-local restoration filters and Fuzzy logic based filters are used to eliminate the noise in SAR image [2]. Recently, Wavelet transform based approaches are considered as strong tool to recover SAR image from noisy data [3]. But the issue of preserving the edges of images remains unsolved. To overcome the limitation of wavelet denoising of images of exhibiting large wavelet coefficient even at fine scales, along all important edges of image cuvelet transform came into existence. Wavelet transform requires many coefficients to reconstruct an image. With so many coefficients to estimate, denoising of image faces certain difficulties [4]. These considerations motivate the increasing interest in reliable de-speckling techniques which reduce the speckle and at the same time preserve the structures in the images. However, although the image, de-speckling has been an active field of research for almost thirty years, and a large number of algorithms have been proposed, performance assessment is still an open issue for real SAR image because of the lack of a reference which does not allow introducing objective measurement criteria. I. This paper presents a methodology to enhance the SAR images using Curvelet transform for gray scale images. CURVELET TRANSFORM Canes and Donoho developed Curvelet transform, a powerful multi-scale multi-orientation image decomposition technique [3]. Basically, Curvelets are like ridgelets that occurs at all scales, locations, and orientations [4]. Curvelet transform solves the problem of curved singularities and varies with scale in the degree of localization in orientation [3]. In Curvelet transform, the combination of multiscale ridgelets and spatial bandpass filtering operation are used for the isolation of different scales.