Abstract

In recent years, image denoising algorithms has witnessed remarkable advancements, largely driven by the speedy development of deep learning techniques. Among these advancements, Denoising Convolutional Neural Network (DnCNN) is a milestone, owing to its powerful performance. However, traditional DnCNN architecture heavily rely on local convolutional operations for feature extraction, which inherently limitation restricts its capacity to capture long-range dependencies, potentially leading to the loss of vital structural information within images. To handle with this limitation, we propose a solution dubbed Cross Attention block. The purpose of this specific block is to extract correlations among non-local features from various source inputs, thereby broadens the receptive field and augments the network's capacity to capture structural information. Furthermore, we integrate the Cross Attention block into DnCNN named CADnCNN, which significantly improves the ability to preserve image details and structural integrity in denoising tasks. Experiments have affirmed the effectiveness of our proposed metho

　

Paper

H. Sun, M. Zhang, C. Zhang, Q. Guo, Z. Sun*. CADnCNN: Improving DnCNN Denoising with Cross Attention. 2023 The International Conference on Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM 2023), pp. 245-249, IEEE, 2023.

Architecture

Results

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