IEEE Trans Image Process. 2016 Jun;25(6):2866-2881. doi: 10.1109/TIP.2016.2556618. Epub 2016 Apr 20.
Recently, image pairs, such as noisy and blurred images or infrared and noisy images, have been considered as a solution to provide high-quality photographs under low lighting conditions. In this paper, a new method for decomposing the image pairs into two layers, i.e., the base layer and the detail layer, is proposed for image pair fusion. In the case of infrared and noisy images, simple naive fusion leads to unsatisfactory results due to the discrepancies in brightness and image structures between the image pair. To address this problem, a local contrast-preserving conversion method is first proposed to create a new base layer of the infrared image, which can have visual appearance similar to another base layer, such as the denoised noisy image. Then, a new way of designing three types of detail layers from the given noisy and infrared images is presented. To estimate the noise-free and unknown detail layer from the three designed detail layers, the optimization framework is modeled with residual-based sparsity and patch redundancy priors. To better suppress the noise, an iterative approach that updates the detail layer of the noisy image is adopted via a feedback loop. This proposed layer-based method can also be applied to fuse another noisy and blurred image pair. The experimental results show that the proposed method is effective for solving the image pair fusion problem.
最近，图像对，如噪声图像与模糊图像或红外图像与噪声图像，已被视为在低光照条件下提供高质量照片的一种解决方案。本文提出了一种将图像对分解为两层（即基础层和细节层）的新方法用于图像对融合。在红外图像与噪声图像的情况下，由于图像对之间亮度和图像结构的差异，简单的朴素融合会导致不理想的结果。为了解决这个问题，首先提出了一种局部对比度保持转换方法来创建红外图像的新基础层，该基础层可以具有与另一个基础层（如去噪后的噪声图像）相似的视觉外观。然后，提出了一种从给定的噪声图像和红外图像设计三种细节层的新方法。为了从三个设计的细节层估计无噪声且未知的细节层，利用基于残差的稀疏性和块冗余先验对优化框架进行建模。为了更好地抑制噪声，通过反馈回路采用一种更新噪声图像细节层的迭代方法。所提出的基于层的方法也可应用于融合另一对噪声图像与模糊图像。实验结果表明，该方法对于解决图像对融合问题是有效的。
