To further improve the execution efficiency and denoising effect of the deep image prior(DIP) denoising model, an improved deep image prior(IDIP) denoising model was proposed by improving the original DIP from three aspects, network architecture, network input, and Loss function. Specifically, considering the network architecture, the simple connection adopted in the encoder-decoder backbone network was promoted with complex connection by adding nonlinear features transferring path, which brings benefit to the information modulation and transmission of features between encoder and decoder at the same level. In aspect of the network input, the random tensor was replaced by the preliminary denoised image with better image quality, and the preliminary denoised image can provide more abundant information to the network model, accelerating the convergence speed of network and improving the execution efficiency. Regarding the Loss function, the preliminary denoised image was added as the second target image to improve the guidance ability of the loss function, improving denoising effect significantly. Extensive experiments show that, the proposed IDIP denoising model significantly outperforms original one at various noise levels in terms of denoising effect and execution efficiency, and it also has a better performance than other state-of-the-art methods with regard to denoising effect. © 2022 Chinese Institute of Electronics. All rights reserved.
