a performance gain of $0.44$ dB on SIDD and $0.50$ dB on DND. Furthermore, it is worth noting that CBDNet [@CBDNet] and RIDNet [@RIDNet] use additional training data, yet our method provides significantly better results. For instance, our method achieves $8.94$ dB improvement over CBDNet [@CBDNet] on the SIDD dataset and $1.82$ dB on DND.

In Fig. \[fig:dnd example\] and Fig. \[fig:sidd example\], we present visual comparisons of our results with those of other competing algorithms. It can be seen that our MIRNet is effective in removing real noise and produces perceptually-pleasing and sharp images. Moreover, it is capable of maintaining the spatial smoothness of the homogeneous regions without introducing artifacts. In contrast, most of the other methods either yield over-smooth images and thus sacrifice structural content and fine textural details, or produce images with chroma artifacts and blotchy texture.

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   ![Denoising examples from SIDD [@sidd]. Our method effectively removes real noise from challenging images, while better recovering structural content and fine texture.[]{data-label="fig:sidd example"}](Images/Denoising/SIDD/RGB/SSID_0324_noisy "fig:"){width=".16\textwidth"}    ![Denoising examples from SIDD [@sidd]. Our method effectively removes real noise from challenging images, while better recovering structural content and fine texture.[]{data-label="fig:sidd example"}](Images/Denoising/SIDD/RGB/SSID_0324_CBD "fig:"){width=".16\textwidth"}    ![Denoising examples from SIDD [@sidd]. 