This paper focuses on factors which influence the fidelity, or realness, of images created by image based rendering (IBR) methods. First, we address the issue of large memory requirements of IBR techniques and we analyze necessary number of input images for a scene representation preserving visual fidelity of the output images. Then, we investigate optimal scaling producing output images that a...

In the paper a probabilistic approach to quality assessment of image processing algorithms is proposed. Presented scalar measure can be used for any colour space and gives very similar results regardless on the image content. It can be an interesting supplement to existing image quality metrics in applications where the details of the processing algorithm are known. Its good correlation with su...

For hyperspectral image research, spectral characteristic retainment is more important than the spatial details retainment, so it is necessary to evaluate the spectral influence of hyperspectral image compressed sensing. In this paper, the researchers select a hyperspectral remote sensing image PROBE CHRIS with abundant coastal wetland ground objects to analyze spectral fidelity of wavelet tran...

A global robust M-estimation scheme for maximum a posteriori (MAP) image super-resolution which efficiently addresses the presence of outliers in the low-resolution images is proposed. In iterative MAP image super-resolution, the objective function to be minimized involves the highly resolved image, a parameter controlling the step size of the iterative algorithm, and a parameter weighing the d...

A global robust M-estimation scheme for maximum a posteriori (MAP) image super-resolution, which efficiently addresses the presence of outliers in the low resolution images is proposed in this work. In iterative MAP image super-resolution, the objective function to be minimized involves the highly resolved image, a parameter controlling the step size of the iterative algorithm and a parameter w...

We present a theoretical study of the recovery of an unknown vector x ∈ IR (a signal, an image) from noisy data y ∈ IR by minimizing with respect to x a regularized cost-function F(x, y) = Ψ(x, y) + αΦ(x), where Ψ is a data-fidelity term, Φ is a smooth regularization term and α > 0 is a parameter. Typically, Ψ(x, y) = ‖Ax − y‖ where A is a linear operator. The data-fidelity terms Ψ involved in ...

We present FusedGAN, a deep network for conditional image synthesis with controllable sampling of diverse images. Fidelity, diversity and controllable sampling are the main quality measures of a good image generation model. Most existing models are insufficient in all three aspects. The FusedGAN can perform controllable sampling of diverse images with very high fidelity. We argue that controlla...

Today, while many researchers focus on the improvement of the regularization term in IR algorithms, they pay less concern to the improvement of the fidelity term. In this paper, we hypothesize that improving the fidelity term will further improve IR image quality in low-dose scanning, which typically causes more noise. The purpose of this paper is to systematically test and examine the role of ...