Learning a cross-scale cross-view decoupled denoising network by mining Omni-channel information

Stereo vision systems are increasingly utilized in various applications, however, the presence of noise significantly hampers the quality of the captured images. Traditional denoising methods often fail to address the complex noise patterns in such scenarios, which can adversely affect feature encoding and subsequent processing tasks. This paper introduces a novel stereo denoising approach that leverages cross-view information to enhance the robustness of noise reduction. A Cross-Channel and Spatial Context Information Mining Module is employed to encode long-range spatial dependencies and to bolster inter-channel feature interaction. This module utilizes large convolutional kernels, channel attention mechanisms, and a simple gating structure to enhance feature representation. Our approach relies on an encoder-decoder architecture, which facilitates cross-view and cross-scale feature interactions. The network is trained with a composite loss function that includes both spatial and perceptual domain constraints, ensuring a comprehensive optimization of the denoising process. Extensive experiments conducted on our proposed NoisyST dataset demonstrate the superior performance of our method in terms of noise removal and detail preservation. Notably, the method outperforms existing State-Of-The-Art techniques, as evidenced by its effectiveness in various evaluation metrics.