Adaptive SURELET-Based Image Denoising in Wavelet Domain with Spatially Varying Noise

Image denoising is a critical task in numerous real-world applications. This paper presents an innovative method for image denoising in the wavelet domain, extending the SURELET approach to handle spatially varying noise levels. Traditional methods often assume a constant noise level across the entire image, which is unrealistic in practical scenarios. Our proposed method estimates the noise level locally within small neighborhoods in the wavelet domain, adapting well to images with spatially varying noise. This approach effectively reduces both uniform and spatially varying noise, as demonstrated through extensive experiments on six test images with five distinct noise patterns. The results, evaluated using peak signal-to-noise ratio (PSNR), show that our method outperforms existing denoising techniques, particularly in scenarios with spatially varying noise. This study not only advances the state-of-the-art in image denoising but also highlights the importance of adaptive noise estimation in real-world applications.