电子学报 ›› 2022, Vol. 50 ›› Issue (7): 1573-1578.DOI: 10.12263/DZXB.20211323

• 学术论文 • 上一篇    下一篇

一种利用改进深度图像先验构建的图像降噪模型

徐少平(), 李芬, 陈孝国, 陈晓军, 江顺亮   

  1. 南昌大学数学与计算机学院,江西 南昌 330031
  • 收稿日期:2021-09-28 修回日期:2022-03-06 出版日期:2022-07-25 发布日期:2022-07-30
  • 通讯作者: 徐少平
  • 作者简介:徐少平 男,1976年5月出生于江西省九江市.博士,南昌大学数学与计算机学院计算机科学与技术系教授,博士生导师.主要研究方向为图形图像处理、机器视觉、虚拟手术仿真等.
    李芬 女,1995年8月出生于江西省南昌市. 现为南昌大学硕士研究生,主要研究方向为图像处理与计算机视觉.E-mail: 411014519034@email.ncu.edu.cn
  • 基金资助:
    国家自然科学基金(62162043)

An Image Denoising Model Using the Improved Deep Image Prior

XU Shao-ping(), LI Fen, CHEN Xiao-guo, CHEN Xiao-jun, JIANG Shun-liang   

  1. School of Mathematics and Computer Sciences,Nanchang University,Nanchang,Jiangxi 330031,China
  • Received:2021-09-28 Revised:2022-03-06 Online:2022-07-25 Published:2022-07-30
  • Contact: XU Shao-ping

摘要:

为进一步提高深度图像先验(Deep Image Prior,DIP)降噪模型的降噪效果和执行效率,从网络结构、网络输入和Loss函数三个方面对其进行改进从而获得了一种改进的深度图像先验(Improved Deep Image Prior,IDIP)降噪模型.具体地,在网络结构方面,通过新增非线性特征传递路径的方法将原DIP模型编码器-解码器(encoder-decoder)架构中相同尺度特征层之间所采用的简单连接改进为复杂连接,有利于特征信息调制与传递从而提高神经网络的非线性映射能力;在网络输入方面,用已具有较高图像质量的初步降噪图像替换随机张量作为网络输入向网络模型提供更为丰富的信息,有利于加快网络收敛速度进而提高执行效率;在Loss函数方面,在原噪声图像的基础上新增初步降噪图像作为第二目标图像,有利于提高Loss函数的导向能力从而提高降噪效果.实验结果表明:所提出的IDIP降噪模型在各噪声水平值下的降噪性能和执行效率均显著优于原DIP模型;与现有的主流降噪方法相比,IDIP降噪模型也具有更好的降噪效果.

关键词: 深度图像先验, 降噪效果, 执行效率, 复杂连接, 混合Loss

Abstract:

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.

Key words: deep image prior, denoising effect, execution efficiency, complex connection, hybrid loss

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