Variational Image Fusion Research Articles

Variational image fusion approach based on TGV and local information

In this study, the authors propose a variational approach based on total generalised variation (TGV) and local gradient information to fuse multi‐focus images as well as medical images of computed tomography and magnetic resonance. They use the second‐order TGV as the regularisation term and local gradient information as the fusion weight to extract image features. To compute the new model effectively, the primal‐dual algorithm is carried out. Various experiments are made to verify the effectiveness of the proposed methods.

Variational Image Fusion with First and Second-Order Gradient Information

图象熔化在主要目标把一样的景色的几幅来源图象集成到一幅更增进知识的图象的计算机视觉是重要的。在这份报纸，我们建议一个变化图象熔化方法基于第一并且秒顺序坡度信息。第一，我们由一个新、简单的凸起标准从来源图象选择一阶的目标和秒顺序坡度信息。然后，我们由要求造我们的模型一阶并且有目标坡度信息的熔化图象匹配的秒顺序坡度信息，并且同时，熔化图象接近来源图象。理论上，我们能证明我们的变化模型有唯一的 minimizer。在数字实现，我们拿裂口 Bregman 方法的使用得到一个有效算法。而且，四方向的差别计划被建议到分离坡度操作员，它能戏剧性地提高熔化质量。有一些流行存在方法的很多个实验和比较证明建议模型在各种各样的图象熔化应用是有希望的。 '

Variational Image Fusion with Optimal Local Contrast

AbstractIn this paper, we present a general variational method for image fusion. In particular, we combine different images of the same subject to a single composite that offers optimal exposedness, saturation and local contrast. Previous research approaches this task by first pre‐computing application‐specific weights based on the input, and then combining these weights with the images to the final composite later on. In contrast, we design our model assumptions directly on the fusion result. To this end, we formulate the output image as a convex combination of the input and incorporate concepts from perceptually inspired contrast enhancement such as a local and non‐linear response. This output‐driven approach is the key to the versatility of our general image fusion model. In this regard, we demonstrate the performance of our fusion scheme with several applications such as exposure fusion, multispectral imaging and decolourization. For all application domains, we conduct thorough validations that illustrate the improvements compared to state‐of‐the‐art approaches that are tailored to the individual tasks.

Efficient Convex Optimization Approaches to Variational Image Fusion

Efficient Convex Optimization Approaches to Variational Image Fusion

Efficient Convex Optimization Approaches to Variational Image Fusion

Efficient Convex Optimization Approaches to Variational Image Fusion

Frame Rate Up Conversion Based on Variational Image Fusion

This paper presents a new framework for motion compensated frame rate up conversion (FRUC) based on variational image fusion. The proposed algorithm consists of two steps: 1) generation of multiple intermediate interpolated frames and 2) fusion of those intermediate frames. In the first step, we determine four different sets of the motion vector field using four neighboring frames. We then generate intermediate interpolated frames corresponding to the determined four sets of the motion vector field, respectively. Multiple sets of the motion vector field are used to solve the occlusion problem in motion estimation. In the second step, the four intermediate interpolated frames are fused into a single frame via a variational image fusion process. For effective fusion, we determine fusion weights for each intermediate interpolated frame by minimizing the energy, which consists of a weighted-L1-norm based data energy and gradient-driven smoothness energy. Experimental results demonstrate that the proposed algorithm improves the performance of FRUC compared with the existing algorithms.