Decolorization is a fundamental technology indispensable for image processing, digital printing, and photograph rendering, while it suffers from information loss. This paper presents a novel parametric ratio-based method for efficient contrast-...

Conventional inpainting methods generally apply textures that are most similar to the areas around the missing region or use large image database. Recently, low rank property of data shows that the non-convex optimization decreases measurements. In this ...

This paper presents a novel Log-Euclidean metric inspired color-to-gray conversion model for faithfully preserving the contrast details of color image, which differs from the traditional Euclidean metric approaches. In the proposed model, motivated by the ...

Most of low-rank tensor approximation problems are NP-hard. Hence a great number of synthesis tensor decomposition approximation have been proposed. In this paper, we instead present an analysis-operator guided tensor decomposition. The proposed method ...

Image prior information is a determinative factor to tackle with the ill-posed problem. In this paper, we present a multi-filters guided low-rank tensor coding (MF-LRTC) model for image restoration. The appeal of constructing a low-rank tensor is obvious ...

Synthesis learning and analysis learning, with sparse coding (SC) and Markov random fields (MRFs) as two representative types of models, are two complementary tools to describe the image manifolds. SC has strengths in representing the regular features/...

While the RGB2GRAY conversion with fixed parameters is a classical and widely used tool for image decolorization, recent studies showed that adapting weighting parameters in a two-order multivariance polynomial model has great potential to improve the ...

The RGB2GRAY conversion model is the classical and most popularly used tool for image decolorization. Recent researches have validated that optimally selecting the three weighting parameters in this first-order linear model has great potential to ...

Recently, two piecewise smooth models L0smoothing and relative total variation (RTV) have been proposed for feature/structure-preserving filtering. One is very efficient for tackling image with little texture patterns and the other has appearance ...

In order to detect the number of audio sources and improve the speech recognition capability of an intelligent robot auditory system, recognizing human mouth-states, open or closed, is studied in this paper. A discriminative dictionary and sparse ...

This paper presents a novel gradient correlation similarity (Gcs) measure-based decolorization model for faithfully preserving the appearance of the original color image. Contrary to the conventional data-fidelity term consisting of gradient error-norm-...

Nonconvex optimization has shown that it needs substantially fewer measurements than l₁ minimization for exact recovery under fixed transform/overcomplete dictionary. In this work, two efficient numerical algorithms which are unified by the method named ...

This paper presents a novel structure gradient and texture decor relating regularization (SGTD) for image decomposition. The motivation of the idea is under the assumption that the structure gradient and texture components should be properly decor ...

Dictionary learning based sparse modeling has been increasingly recognized as providing high performance in the restoration of noisy images. Although a number of dictionary learning algorithms have been developed, most of them attack this learning ...

Image recovery from undersampled data has always been challenging due to its implicit ill-posed nature but becomes fascinating with the emerging compressed sensing (CS) theory. This paper proposes a novel gradient based dictionary learning method for ...

To effectively remove impulse noise in natural images while keeping image details intact, this paper proposes a dictionary learning based impulse noise removal (DL-INR) algorithm, which explores both the strength of the patch-wise adaptive dictionary ...

This paper presents an efficient alternating direction method with patch-based dictionary updating, ADMDU-DEB, for sparse representation regularization framework of image deblurring. The main idea of the proposed method is to reformulate the variational ...

The nonlocal means (NLM) filter has distinct advantages over traditional image denoising techniques. However, in spite of its simplicity, the pixel value-based self-similarity measure used by the NLM filter is intrinsically less robust when applied to ...

This paper presents an augmented Lagrangian (AL) based method for designing of overcomplete dictionaries for sparse representation with general l"q-data fidelity term (q=<2). In the proposed method, the dictionary is updated via a simple gradient ...

Dictionary learning has been a hot topic fascinating many researchers in recent years. Most of existing methods have a common character that the sequences of learned dictionaries are simpler and simpler regularly by minimizing some cost function. This ...