Heyan Huang

• Shanghai Institute of Technology

Improving imaging quality and reducing time consumption are the key problems that need to be solved in the practical application of ghost imaging. Hence, we demonstrate a double filter iterative ghost imaging method, which adopts the joint iteration of projected Landweber iterative regularization and double filtering based on block matching three d...

The realization of high-quality imaging under low sampling is an effective way to solve the practical application of ghost imaging. In this paper, we present an advanced framework of compressed ghost imaging under low sampling. During the imaging process, the regularization and mutual structure filtering operations are performed alternately, which...

Ghost imaging has great application potential in remote sensing observation and biomedical due to the use of a single pixel detector for imaging, which makes it easy to imaging in low light conditions and imperfect spectral regions of the camera. However, the higher the reconstructed image resolution (the more pixels), the larger the number of meas...

This paper proposes an extension of the non-local Bayesian denoising algorithm. The idea is to use elliptical patches instead of regular square patches in the grouping process. We calculate the elliptical patches by an iterative method. We then use an affine invariant patch similarity measure to calculate the distance between two elliptical patches...

Multi-resolution imaging is one of the key means to obtain the target scene information. The Hadamard matrix, which is orthogonal, is an important modulation matrix for single-pixel imaging. In particular, it can provide a good means for multi-resolution imaging. However, as far as we know, studies of high-efficiency multi-resolution single-pixel i...

High-quality ghost imaging (GI) under low sampling is very important for scientific research and practical application. How to reconstruct high-quality image from low sampling has always been the focus of ghost imaging research. In this work, based on the hypothesis that the matrix stacked by the vectors of image’s nonlocal similar patches is of lo...

The Hadamard matrix with orthogonality is a more important modulation matrix for computational ghost imaging (CGI), especially its optimized Hadamard matrix. However, as far as we know, little mention has been paid to efficient and convenient Hadamard matrix generation for CGI. The existing methods are to reconstruct any row of Hadamard matrix into...

Imaging and edge detection have been widely applied and played an important role in security checking and medical diagnosis. However, as we know, most edge detection based on ghost imaging system requires large measurement times and the target object image cannot be provided directly. In this work, a new edge detection based on joint iteration of p...

Imaging and edge detection have been widely applied and played an important role in security checking and medical diagnosis. However, as we know, most edge detection based on ghost imaging system require a large measurement times and the target object image cannot be provided directly. In this work, a new edge detection based on joint iteration of...

We propose a high-quality compressive ghost imaging method based on projected Landweber regularization and guided filter, which effectively reduce the undersampling noise and improve the resolution. In our scheme, the original object is reconstructed by decomposing of regularization and denoising steps instead of solving a minimization problem in c...

The image with rich textures can be decomposed into the sum of a geometric part and a textural part. Inspired by this fact, we propose an efficient texture-preserving image deconvolution algorithm based on image decomposition. Our algorithm restores the geometric part and textural part, respectively, by incorporating \(L_0\) gradient minimization a...

Magnetic resonance imaging (MRI) reconstruction from the smallest possible set of Fourier samples has been a difficult problem in medical imaging field. In our paper, we present a new approach based on a guided filter for efficient MRI recovery algorithm. The guided filter is an edge-preserving smoothing operator and has better behaviors near edges...

A method of the hybrid speckle-pattern compressive computational ghost imaging scheme is proposed. The scheme detects the larger and smaller resolution areas of the object via identifying complex object composed of different resolution scales automatically. The hybrid speckle pattern composed of different sizes of speckles is generated according to...

In this work, we propose a new approach for efficient edge-preserving image deconvolution. Our algorithm is based on a novel type of explicit image filter - guided filter. The guided filter can be used as an edge-preserving smoothing operator like the popular bilateral filter, but has better behaviors near edges. We propose an efficient iterative a...

In this paper, we propose a new dictionary learning approach for image deconvolution, which effectively integrates the Fourier regularization and dictionary learning technique into the deconvolution framework. Specifically, we propose an iterative algorithm with the decoupling of the deblurring and denoising steps in the restoration process. In the...

In this paper, an efficient image deblurring algorithm is proposed. This algorithm restores the blurred image by incorporating a curvelet-based empirical Wiener filter with a spatial-based joint non-local means filter. Curvelets provide a multidirectional and multiscale decomposition that has been mathematically shown to represent distributed disco...

In this paper, we propose a new approach for performing efficient edge-preserving image deconvolution algorithm based on a nonlocal domain transform (NLDT). We present the geodesic distance-preserving transforming procedure of a 1D signal embedded in 2D space into a new 1D domain via a transformation for simplicity. The nonlocal domain transform de...

In this paper we propose an approach for handling noise in deconvolution algorithm based on multidirectional filters. Most image deconvolution techniques are sensitive to the noise. Even a small amount of noise will degrade the quality of image estimation dramatically. We found that by applying a directional low-pass filter to the blurred image, we...

Image restoration and deconvolution from blurry and noisy observation is known to be ill-posed. To stabilize the recovery, total variation (TV) regularization is often utilized for its beneficial edge in preserving the image's property. We take a different approach of TV regularization for image restoration. We first recover horizontal and vertical...

Robustness is difficult to resolve in digital watermarking research, and contradicts its stealthiness. So the key of designing robust digital watermarking is selecting watermark embedding positions. Around these problems, we study the robust digital watermarking algorithm based on DWT. By wavelet transform, image smoothing based on PDE, and morphol...