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Image denoising is a fundamental problem in image processing whose primaryobjective is to remove the noise while preserving the original image structure.In this work, we proposed a new architecture for image denoising. We have usedseveral dense blocks to design our network. Additionally, we have forwardedfeature extracted in the first layer to the input of every transition layer.Our experimental result suggests that the use of low-level feature helps inreconstructing better texture. Furthermore, we had trained our network with acombination of MSE and a differentiable multi-scale structural similarityindex(MS-SSIM). With proper training, our proposed model with a much lowerparameter can outperform other models which were with trained much higherparameters. We evaluated our algorithm on two grayscale benchmark dataset BSD68and SET12. Our model had achieved similar PSNR with the current state of theart methods and most of the time better SSIM than other algorithms.

A lightweight convolutional neural network for image denoising with fine details preservation capability