Information-Enhanced Image Denoising Method Based on Deep Learning

Image denoising is a fundamental task in computer vision, essential for enhancing visual quality and facilitating downstream applications. Traditional denoising methods often rely on handcrafted priors or statistical assumptions, which limit their effectiveness in real-world scenarios with complex noise patterns. Recent advances in deep learning have led to significant improvements by leveraging data-driven approaches. In this paper, we propose an information-enhanced image denoising framework based on deep convolutional neural networks (CNNs), which incorporates both spatial and frequency-domain features to improve denoising performance. Our method integrates a dual-branch architecture: one branch extracts spatial features using a residual CNN, while the other encodes frequency components through discrete wavelet transform (DWT) and learns texture-aware representations. A feature fusion module adaptively combines multi-scale information, enabling robust noise suppression while preserving fine structural details. Extensive experiments on standard benchmarks such as BSD68, Set12, and Urban100 demonstrate that our approach outperforms state-of-the-art denoising methods in terms of PSNR and SSIM, particularly under challenging noise levels and non-Gaussian conditions. The results confirm the effectiveness of information enhancement in deep learning-based image denoising.