QformerID: Quaternion Transformer-Based Image Denoising
The Van Le, Jin Young Lee · IEEE Multimedia · 2024
Image denoising is a process of removing noise from an image to improve its quality and clarity. Many state-of-the-art networks utilize real-valued representation, which can miss cross-correlation of RGB channels in a color image. To tackle this limitation, this article proposes Quaternion transformer-based image denoising (QformerID). Specifically, it includes a novel Quaternion transformer layer that effectively learns long-range correlations between pixels and preserves the cross-correlation of RGB channels, based on Quaternion representation. Experimental results show that QformerID offers higher performance on various datasets, including Gaussian and real-world noisy images in terms of objective and subjective qualities, compared with state-of-the-art networks. In addition, visual assessment shows that QformerID not only preserves image details but also suppresses noise very well.