Denoising of Synthetic Aperture Radar Images Using Dual Tree Curved Wavelet Transform with Modified Cellular Neural Networks

Synthetic aperture radar, sometimes known simply as SAR, is a technique for imaging a target from space that uses microwave radiations to illuminate the object that is the focus of the picture. These brief bursts of microwave radiation are sent with the help of a piece of equipment known as RADAR. Denoising is an important pre-treatment step in image processing that has to be finished before an application-friendly picture can be produced. This step is necessary to create an image. They are versatile enough to be used for everything from digital photography to photographing satellites. Their range of applications is rather extensive. The imaging method known as synthetic aperture radar may be used in any weather, both during the day and at night, and it uses airborne radar to illuminate the earth’s surface. Satellites inspired the development of this particular technology. Processing must first be performed on the recorded backscattered signals before synthetic aperture radar images can be produced using them. Because microwaves can travel through clouds and dirt, they are often used in settings where traditional imaging would be difficult. Speckle noise is a natural occurrence that may be observed as granular patterns in synthetic aperture radar images. These images are produced by synthetic aperture radar (SAR) technology. It could be difficult to eliminate speckles, a random multiplicative noise. Speckle is a sort of noise. Speckle is a kind of noise that occurs in coherent systems and is formed when echoes interact with transmitted signals. This interaction may result in either constructive or destructive interference. The appearance of the speckle lowers the overall image quality, which means that the application cannot use it since it is improper. Denoising SAR pictures is a tough approach, but it is important since there is noise in the images and it has to be removed. Denoising is a strategy that should be used to eliminate the noise, but all of the essential visual information should be preserved. Denoising may be done in either the spatial domain or the transform domain, both of which are acceptable choices. Combining the dual tree curved wavelet transform with modified cellular neural networks is said to produce a one-of-a-kind filter, which has both been hypothesized and produced as a possibility (DTCWT-MCNN). The recommended filter is implemented using SAR images and put through its paces to see how well it works. The recommended approach is given a score based on its efficacy, which is determined by utilizing objective metrics, and its performance is analysed to see how effectively it functions. Performance evaluation metrics such as Noise Mean Value (NMV), Mean Square Difference (MSD), Equal Number of Looks (ENL), Noise Standard Deviation (NSD), and Speckle Suppression Index were utilized to carry out quantitative confirmation of the findings. These metrics were utilized to confirm the findings (SSI).