Image compressed sensing using convolutional neural network
In the study of compressed sensing (CS), the two main challenges are the design of
sampling matrix and the development of reconstruction method. On the one hand, the …
sampling matrix and the development of reconstruction method. On the one hand, the …
Content-aware convolutional neural network for in-loop filtering in high efficiency video coding
Recently, convolutional neural network (CNN) has attracted tremendous attention and has
achieved great success in many image processing tasks. In this paper, we focus on CNN …
achieved great success in many image processing tasks. In this paper, we focus on CNN …
Scalable convolutional neural network for image compressed sensing
Recently, deep learning based image Compressed Sensing (CS) methods have been
proposed and demonstrated superior reconstruction quality with low computational …
proposed and demonstrated superior reconstruction quality with low computational …
[HTML][HTML] Exemplar-based image inpainting using angle-aware patch matching
N Zhang, H Ji, L Liu, G Wang - EURASIP Journal on Image and Video …, 2019 - Springer
Image inpainting has been presented to complete missing content according to the content
of the known region. This paper proposes a novel and efficient algorithm for image …
of the known region. This paper proposes a novel and efficient algorithm for image …
From rank estimation to rank approximation: Rank residual constraint for image restoration
In this paper, we propose a novel approach to the rank minimization problem, termed rank
residual constraint (RRC) model. Different from existing low-rank based approaches, such …
residual constraint (RRC) model. Different from existing low-rank based approaches, such …
Global spatial and local spectral similarity-based manifold learning group sparse representation for hyperspectral imagery classification
Spectral-spatial framework has been widely applied for hyperspectral image classification
task. Some well-established models, such as group sparse representation (GSR), have …
task. Some well-established models, such as group sparse representation (GSR), have …
A corrected tensor nuclear norm minimization method for noisy low-rank tensor completion
In this paper, we study the problem of low-rank tensor recovery from limited sampling with
noisy observations for third-order tensors. A tensor nuclear norm method based on a convex …
noisy observations for third-order tensors. A tensor nuclear norm method based on a convex …
Spectral CT reconstruction—ASSIST: Aided by self-similarity in image-spectral tensors
Spectral computed tomography (CT) reconstructs multienergy images from data in different
energy bins. However, these reconstructed images can be contaminated by noise due to the …
energy bins. However, these reconstructed images can be contaminated by noise due to the …
Tchebichef and adaptive steerable-based total variation model for image denoising
Structural information, in particular, the edges present in an image, is the most important part
to be noticed by human eyes. Therefore, it is important to denoise this information effectively …
to be noticed by human eyes. Therefore, it is important to denoise this information effectively …
Pairwise-comparison-based rank learning for benchmarking image restoration algorithms
Image restoration has attracted substantial attention recently and many image restoration
algorithms have been proposed for restoring latent clear images from degraded images …
algorithms have been proposed for restoring latent clear images from degraded images …