Meng Li’s research while affiliated with Xi’an University of Posts and Telecommunications and other places

What is this page?


This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (2)


Task of image upscaling
Illustration of the blurring image
(a) Blurred image, (b) Desired image
Illustration of the jagged artefacts
(a) Degraded image with jagged artefacts, (b) Image enhanced by the IBP algorithm, (c) Image enhanced by the traditional TV regularisation algorithm, (d) Image enhanced by the proposed algorithm, (e) Desired image
Illustration of the screw artefact
(a) Degraded image with the screw artefact, (b) Image enhanced by the IBP algorithm, (c) Image enhanced by the traditional TV regularisation algorithm, (d) Image enhanced by the proposed algorithm, (e) Desired image
Illustration of a deforming curve
(a) Curve C with artefacts, (b) Ideal curve C. We can shorten the arc length of the curve C to make it similar to the ideal edge to reduce jagged artefacts

+19

Discarding jagged artifacts in image-upscaling with total variation regularization
  • Article
  • Publisher preview available

October 2019

·

138 Reads

·

3 Citations

Jian Xu

·

Meng Li

·

·

Image upscaling is needed in many areas. There are two types of methods: methods based on a simple hypothesis and methods based on machine learning. Most of the machine learning‐based methods have disadvantages: no support is provided for a variety of upscaling factors, a training process with a high time cost is required, and a large amount of storage space and high‐end equipment are required. To avoid the disadvantages of machine learning, upscaling images with a simple hypothesis is a promising strategy but simple hypothesis always produces jaggy artifacts. The authors propose a new method to remove these jagged artifacts. They consider an edge in an image as a deformed curve. Removing jagged artefacts is considered equivalent to shortening the full arc length of a curve. By optimising the regularization model, the severity of the artifacts decreases as the number of iterations increases. They compare nine existing methods on the Set5, Set14, and Urban100 datasets. Without using any external data, the proposed algorithm has high visual quality, has few jagged artefacts and performs similarly to very recent state‐of‐the‐art deep convolutional network‐based approaches. Compared to other methods without external data, the proposed algorithm balances the quality and time cost well.

View access options

Self-Learning Super-Resolution Using Convolutional Principal Component Analysis and Random Matching

September 2018

·

46 Reads

·

13 Citations

IEEE Transactions on Multimedia

Jian Xu

·

Meng Li

·

·

[...]

·

Zhiguo Chang

Self-learning super-resolution (SLSR) algorithms have the advantage of being independent of an external training database. This paper proposes a self-learning super-resolution algorithm that uses convolutional principal component analysis (CPCA) and random matching. The technologies of CPCA and random matching greatly improve the efficiency of self-learning. There are two main steps in this algorithm: forming the training and testing the data sets and patch matching. In the data set forming step, we propose the CPCA to extract the lowdimensional features of the data set. The CPCA uses a convolutional method to quickly extract the PCA features of each image patch in every training and testing image. In the patch matching step, we propose a two-step random oscillation accompanied with propagation to accelerate the matching process. This patch matching method avoids exhaustive searching by utilizing the local similarity prior of natural images. The two-step random oscillation first performs a coarse patch matching using the variance feature and then performs a detailed matching using the PCA feature, which is useful to find reliable matching patches. The propagation strategy enables patches to propagate the good matching patches to their neighbors. The experimental results demonstrate that the proposed algorithm has a substantially lower time cost than that of many existing self-learning algorithms, leading to better reconstruction quality.

Citations (2)


... Total variation regularization pursues minimizing the total variation over pixels; meanwhile, allowing insignificant or little changes to be made. That is, the method fulfills smoothing the change of pixel values while it still retains edges [15]. ...

Reference:

Image Upscaling with Deep Machine Learning for Energy-Efficient Data Communications
Discarding jagged artifacts in image-upscaling with total variation regularization

... To fairly evaluate the SR performance of DSRCNN, quantitative and qualitative analysis are used to conduct experiments. The quantitative analysis includes PSNR [44] and SSIM [44] of popular methods, i.e., Bicubic, A+ [7], jointly optimized regressors (JOR) [45], RFL [6], self-exemplars super-resolution (SelfEx) [36], CSCN [18], RED [19], a denoising convolutional neural network (DnCNN) [46], trainable nonlinear reaction diffusion (TNRD) [47], fast dilated residual SR convolutional network (FDSR) [48], SRCNN [10], fast SR CNN (FSRCNN) [14], very deep SR network (VDSR) [19], deeply-recursive convolutional network (DRCN) [13], context wise network fusion (CNF) [49], Laplacian SR network (LapSRN) [50], deep persistent memory network (MemNet) [11], CARN-M [22], wavelet domain residual network (WaveResNet) [51], convolutional principal component (CPCA) [52], new architecture of deep recursive convolution networks for SR (NDRCN) [53], LESRCNN [8], LESRCNN-S [8], and DSRCNN on four public datasets, i.e., Set5, Set14, B100, and U100. In terms of quantitative analysis, our proposed DSRCNN has obtained the best SR results in most circumstances as shown in Tables 2-5. ...

Self-Learning Super-Resolution Using Convolutional Principal Component Analysis and Random Matching
  • Citing Article
  • September 2018

IEEE Transactions on Multimedia