Abstract Aiming at the problem of texture loss and poor perceptual quality in low-resolution mural images, this paper proposes a zero-sample superresolution reconstruction method called EPZSSR to enhance quality, specific model is obtained by training image. The algorithm takes zero-shot as framework, randomly cuts original image into 128 * size, performs Gaussian blurring on image, uses Lanczo...

Image fusion is an important visualization technique of integrating coherent spatial and temporal information into a compact form. Laplacian fusion is a process that combines regions of images from different sources into a single fused image based on a salience selection rule for each region. In this paper, we proposed an algorithmic approach using a Laplacian and Gaussian pyramid to better loc...

This paper proposes a new joint decoding algorithm frame based on compressed sensing CS and LDPC (Low-Density Parity-Check) codes. Redundant information can be effectively extracted and amplified by CS reconstruction as a compensation to correct decoding of LDPC codes. We adopt Gaussian kernel function of image segmentation as a reflection. Simulation results indicate, compared with LDPC algori...

In this report we solved a regularized Poisson maximum likelihood (ML) image reconstruction problem, using various numerical methods. Rather than the commonly assumed Gaussian ML formulation, we considered a Poisson ML formulation, which is more accurate in some applications such as low dose computed tomography (CT), and also avoids the problematic log conversion in the Gaussian formulation. Th...

The authors present a Markov random field model which allows realistic edge modeling while providing stable maximum a posterior (MAP) solutions. The model, referred to as a generalized Gaussian Markov random field (GGMRF), is named for its similarity to the generalized Gaussian distribution used in robust detection and estimation. The model satisfies several desirable analytical and computation...

We propose two hard thresholding schemes for image reconstruction from compressive samples. The measurements follow an underdetermined linear model, where the regression-coefficient vector is a sum of an unknown deterministic sparse signal component and a zero-mean white Gaussian component with an unknown variance. We derived an expectation-conditional maximization either (ECME) iteration that ...

Tomographic reconstruction from noisy projections do not yield adequate results. Mathematically, this tomographic reconstruction represents an ill-imposed problem due to information missing caused from the presence of noise. Maximum A Posteriori (MAP) or Bayesian reconstruction methods offer possibilities to improve the image quality as compared with analytical methods in particular by introduc...

where b ∈ RM is a vectorized representation of M measurements, x ∈ RN is the vector of N unknowns, and A ∈ RM×N is the system matrix. In many imaging problems, the measurements are corrupted by noise. Although there are a variety of different sources, such as quantization, saturation, thermal, analog to digital conversion, and compression, typical image reconstruction problems deal with one of ...

In this paper, we describe a prototype system that. reconstructs three-dimensional (3D) blood vessels image from cone-beam projection images and analyzes the 3D reconstructed image. Several 3D reconstruction methods utilize biplane or stereo angiographic system and assume the cross section shapes of blood vessels (e.g. ellipse). However, the information provided by a few projections is insuffic...