In the realm of computer vision, image denoising remains a formidable challenge with profound implications for fields like medical imaging, remote sensing, and photography. Despite notable advancements in deep learning, there are enduring challenges: current convolutional neural networks (CNNs) frequently struggle with training complexities due to their emphasis on increased network depth. At the same time, these networks often fail to adequately consider the crucial role of gradient information in the denoising process. Furthermore, there is a distinct gap in leveraging transform domain analysis in image denoising. This study addresses these limitations with MDFIDNet, a novel triple-phase attentive fusion network tailored for image denoising. MDFIDNet integrates three independent feature extraction pipelines: a frequency domain processing pipeline (FDP) enhanced by a multi-scale convolutional attention Block (MSCAB), a spatial domain processing pipeline (SDP) focusing on detail feature preservation, and a gradient-domain processing pipeline (GDP) driven by multidirectional gradient information. Experimental validation demonstrates that MDFIDNet surpasses existing benchmarks, exhibiting robust performance across diverse datasets. Comprehensive ablation studies underscore the individual contributions of each network component, elucidating the novel advancements that underpin MDFIDNet’s superior denoising efficacy. The source code and further details are available in the  https://github.com/debashis15/MDFIDNet .