Topic
Gradient-domain image processing
About: Gradient-domain image processing is a research topic. Over the lifetime, 5 publications have been published within this topic receiving 121 citations.
Papers
TL;DR: The system matrix for the constant weight problem is an appropriate preconditioner, in the sense that a sub-problem in the PCG is efficiently solved by the FFT and also it ensures the convergent splitting of the system matrix.
Abstract: This article explores an energy function and its minimization for the weighted gradient domain image processing, where variable weights are applied to the data term of conventional function for attaining better results in some applications. To be specific, larger weights are given to the regions where original pixel values need to be kept unchanged, like strong edge regions in the case of image sharpening application or high contrast regions when fusing multi-exposure images. In the literatures, it is shown that the solution to a constant weight problem can be efficiently obtained in the frequency domain without iterations, whereas the function with the varying weights can be minimized by solving a large sparse linear equation or by iterative methods such as conjugate gradient or preconditioned conjugate gradient (PCG) methods. In addition to introducing weighted gradient domain image processing problems, we also proposed a new approach to finding an efficient preconditioning matrix for this problem, which greatly reduces the condition number of the system matrix and thus reduces the number of iterations for the PCG process to reach the solution. We show that the system matrix for the constant weight problem is an appropriate preconditioner, in the sense that a sub-problem in the PCG is efficiently solved by the FFT and also it ensures the convergent splitting of the system matrix. For the simulation and experiments on some applications, it is shown that the proposed method requires less iteration, memory, and CPU time.
6 citations
1 Sep 2012
TL;DR: This paper proposes new intra and inter-deinterlacing algorithm based on the gradient domain image/video processing approach, which interpolates the gradients of missing pixels along the optimal spatial orientation.
Abstract: This paper proposes new intra and inter-deinterlacing algorithm based on the gradient domain image/video processing approach. From the interlaced (field) images, gradient field images are generated and then the gradients of missing lines are estimated to generate gradient images which correspond to progressive frames. The proposed intra-deinterlacing is basically an edge-oriented interpolation, which interpolates the gradients of missing pixels along the optimal spatial orientation. Finding the optimal orientation among all possible ones is formulated as a labeling problem with Markov random field (MRF) framework. For obtaining better results for fast moving video sequences, this method is extended to inter-deinterlacing, which considers the temporal orientations as well as the spatial ones. With the synthesized gradient frame images and the original pixel values of the field images, we then formulate a linear equation that generates the final progressive frame images. Like other gradient domain image processing applications, the integrity of edges is the main advantage of the proposed method.
3 citations
29 Dec 2011
TL;DR: A novel contrast enhancement algorithm with histogram-guided boundary conditions and an efficient masking scheme, called the soft masking, to strike the balance between the global enhancement and the local enhancement.
Abstract: A novel contrast enhancement algorithm with histogram-guided boundary conditions is proposed in this work. The proposed algorithm enhances details in local regions by boosting gradient components, while improving the overall contrast by imposing boundary conditions based on a global transformation function. Moreover, we develop an efficient masking scheme, called the soft masking, to strike the balance between the global enhancement and the local enhancement. Simulation results demonstrate that the proposed algorithm can yield high quality output images by improving both global and local contrast simultaneously.
2 citations
TL;DR: In this paper, a variational formulation of anisotropic gradient-domain image processing has been proposed, which minimizes the eigenvalues of the structure tensor of the difference between the processed gradient and the original image.
Abstract: Gradient-domain image processing is a technique where, instead of operating directly on the image pixel values, the gradient of the image is computed and processed. The resulting image is obtained by reintegrating the processed gradient. This is normally done by solving the Poisson equation, most often by means of a finite difference implementation of the gradient descent method. However, this technique in some cases lead to severe haloing artefacts in the resulting image. To deal with this, local or anisotropic diffusion has been added as an ad hoc modification of the Poisson equation. In this paper, we show that a version of anisotropic gradient-domain image processing can result from a more general variational formulation through the minimisation of a functional formulated in terms of the eigenvalues of the structure tensor of the differences between the processed gradient and the gradient of the original image. Example applications of linear and nonlinear local contrast enhancement and colour image Daltonisation illustrate the behaviour of the method.
4 Aug 2007
TL;DR: This paper develops a method to identify the relevant image structures and their importance, and proposes tools to manipulate this structure in a flexible way, and employs gradient domain image processing techniques to reconstruct the final image from a "cropped" gradient information.
Abstract: Visual content is often better communicated by simplified or exaggerated images than by the "real world like" images. In this paper, we offer a tool for creating such enhanced representations of photographs in a way consistent with the original image content. To do so, we develop a method to identify the relevant image structures and their importance. Our approach (a) uses edges as the basic structural unit in the image, (b) proposes tools to manipulate this structure in a flexible way, and (c) employs gradient domain image processing techniques to reconstruct the final image from a "cropped" gradient information. This edge-based approach to non-photorealistic image processing is made feasible by two new techniques we introduce: an addition to the Gaussian scale space theory to compute a perceptually meaningful hierarchy of structures, and a contrast estimation method necessary for faithful gradient-based reconstructions. We finally present various applications that manipulate image structure in different ways.
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 1 |
| 2013 | 1 |
| 2012 | 1 |
| 2011 | 1 |
| 2007 | 1 |