Enhancing Satellite Imagery: A Novel Approach to Gaussian Noise Reduction Using Convolutional Neural Networks and Nonlinear Filtering Techniques
Mohammed Abdul Wadood Mohammed Mohammed, Assma Ghalib Jaber Ghalib Jaber · Iraqi statisticians journal. · 2025
Image denoising is one of the fundamental aspects of removing noise from an image and enhancing its features containing visual information. Based on this, Convolutional Neural Networks (CNNs) have been a latest topic of study, with a wide range of applications in fields as diverse as diagnostic image denoising and low-light image denoising. In this paper, an image denoising method is proposed based on converting the noisy image to YUV colon space, extracting the noisy Y channel, and obtaining an appropriate smoothing parameter for the noisy Y channel using the cross-validation smoothing techniques that are used to estimate the Y density function, then using an appropriate noise reduction method (total variation denoising) on the estimated density function to extract the denoised Y channel by removing the Gaussian noise. The results showed that the new proposed method effectively removed noise from the image, which is attributed to the approach adopted in this proposed filter.