Implementation of interpolation method in reconstructing damaged satellite image caused by impulse noise

Background: Images are extensively utilized in fields such as engineering, health, and defense. During transmission, these images often lose quality due to noise interference.Aim: The primary objective of this study is to develop a method to effectively reduce salt and pepper noise, a common issue in image transmission, and restore images to their original state.Method: To achieve this, we propose using a numerical approach based on the interpolation method, specifically designed to address the noise reduction challenge.Result: Experimental application of the interpolation method on various images demonstrated that it significantly enhances the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) values, especially for images with low to medium noise density.Conclusion: Compared to other methods, our interpolation-based approach shows superior performance in reducing salt and pepper noise in images, making it a promising solution for image restoration in various applications

Recursive trimmed filter in eliminating high density impulse noise from digital image

Advances in technology have made it easier to share media over the Internet. In the process of media sharing, a media may receive noise or interference that results in loss of information. In this paper, a new method to remove Salt and Pepper noise from images based on recursive method will be presented. The first stage is to recognize the noise from the damaged image, the damaged pixels will be replaced by the mean of the surrounding window, the difference with other methods is the use of recursive approach that aims to minimize the size of the window in the recovery process

A random exploration based fast adaptive and selective mean filter for salt and pepper noise removal in satellite digital images

The digital image is one of the discoveries that play an important role in various aspects of modern human life. These findings are useful in various fields, including defense (military and non-military), security, health, education, and others. In practice, the image acquisition process often suffers from problems, both in the process of capturing and transmitting images. Among the problems is the appearance of noise which results in the degradation of information in the image and thus disrupts further processes of image processing. One type of noise that damages digital images is salt and pepper noise which randomly changes the pixel values to 0 (black) or 255 (white). Researchers have proposed several methods to deal with this type of noise, including median filter, adaptive mean filter, switching median filter, modified decision based unsymmetric trimmed median filter, and different applied median filter. However, this method suffers from a decrease in performance when applied to images with high-intensity noise. Therefore, in this research, a new filtering method is proposed that can improve the image by randomly exploring pixels, then collecting the surrounding pixel data from the processed pixels (kernel). The kernel will be enlarged if there are no free-noise pixels in the kernel. Furthermore, the damaged pixels will be replaced using the mean data centering statistic. Images enhanced using the proposed method have better quality than the previous methods, both quantitatively (SSIM and PSNR) and qualitatively

A Two-Stage Filter for High Density Salt and Pepper Denoising

Image restoration is an important and interesting problem in the field of image processing because it improves the quality of input images, which facilitates postprocessing tasks. The salt-and-pepper noise has a simpler structure than other noises, such as Gaussian and Poisson noises, but is a very common type of noise caused by many electronic devices. In this article, we propose a two-stage filter to remove high-density salt-and-pepper noise on images. The range of application of the proposed denoising method goes from low-density to high-density corrupted images. In the experiments, we assessed the image quality after denoising using the peak signal-to-noise ratio and structural similarity metric. We also compared our method against other similar state-of-the-art denoising methods to prove its effectiveness for salt and pepper noise removal. From the findings, one can conclude that the proposed method can successfully remove super-high-density noise with noise level above 90%. (c) 2020, Springer Science+Business Media, LLC, part of Springer Nature