Blind denoising algorithm for RAW images based on DnCNN IID

DOI:	10.11809/bqzbgcxb2024.08.025
Keywords:	RAW Image;convolutional neural network; blind noise reduction; dark image enhancement
Abstract:	Due to the overall dark, insufficient contrast, and inconsistent brightness in RAW images, the noise in RAW images is complex and diverse. In this paper, an improved Denoising model DnCNN IID (Denoising Convolutional Neural Network with Image Inversion and Down sample) is proposed to suppress complex noise in RAW images and enhance image quality. The model is based on DnCNN network, and the image was processed by the image inversion for data enhancement to improve the image contrast, highlight the details and edges in the image, and highlight the characteristic information of the noise. By adding image down sampling, the network processing efficiency is improved, the network receptive field is expanded, the model’s ability to perceive the global information of the input image is improved, and the noise is suppressed more effectively. To verify the effectiveness of the algorithm, the proposed model is compared with the mainstream methods on the BSD500 dataset, Ex/600 dataset and RAW dataset. The experimental results show that the proposed model has a better improvement in PSNR, SSIM and MSE.
Issue:	Vol. 45 No. 8 (2024)
Published:	2024-09-02
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