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Author

Liwen He

Bio: Liwen He is an academic researcher from Nanjing University of Posts and Telecommunications. The author has contributed to research in topics: Stairstep interpolation & Multivariate interpolation. The author has an hindex of 3, co-authored 6 publications receiving 56 citations.

Papers
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Journal ArticleDOI
TL;DR: Zhang et al. as mentioned in this paper proposed a multidirectional weighted interpolation algorithm for color filter array interpolation, which exploits to greater degree correlations among neighboring pixels along eight directions to improve the interpolation performance.
Abstract: This paper presents a novel multidirectional weighted interpolation algorithm for color filter array interpolation. Our proposed method has two contributions to demosaicking. First, different from conventional interpolation methods based on two directions or four directions, the proposed method exploits to greater degree correlations among neighboring pixels along eight directions to improve the interpolation performance. Second, we propose an efficient postprocessing method to reduce interpolation artifacts based on the color difference planes. Compared with conventional state-of-the-art demosaicking algorithms, our experimental results show the proposed algorithm provides superior performance in both objective and subjective image quality. Furthermore, this implementation has moderate computational complexity.

43 citations

Journal ArticleDOI
TL;DR: Compared with the latest demosaicking algorithms, experiments showed that the proposed method provides superior performance in terms of both objective and subjective image qualities.

7 citations

Journal ArticleDOI
TL;DR: A novel color image demosaicking algorithm based on a directional weighted interpolation method and gradient inverse-weighted filter-based refinement method that provides superior performance in terms of both objective and subjective image quality compared to conventional state-of-the-art demosaicks algorithms.

4 citations

Proceedings ArticleDOI
08 Oct 2015
TL;DR: The proposed algorithm provided superior performance in terms of both objective and subjective image quality compared to conventional directional weighted Demosaicking algorithms and has very low complexity and is therefore well suited for real-time applications.
Abstract: In this paper, we present an improved directional weighted interpolation method for single-sensor camera imaging. By observing the fact that the conventional directional weighted interpolation methods are based on unreliable assumptions using spectral correlation, a contribution of this work is made using an anti-aliasing finite impulse response filter to improve the interpolation accuracy by exploiting robust spectral correlation. We also make a contribution towards refining the interpolation result by using the gradient inverse weighted filtering method. An experimental analysis of images revealed that our proposed algorithm provided superior performance in terms of both objective and subjective image quality compared to conventional directional weighted Demosaicking algorithms. Our implementation has very low complexity and is therefore well suited for real-time applications.

3 citations

Journal ArticleDOI
TL;DR: An experimental analysis of images revealed that the proposed algorithm provided superior performance in terms of both objective and subjective image quality compared to conventional directional weighted demosaicking algorithms, and is well suited for real-time applications.
Abstract: We present an improved directional weighted interpolation method for single-sensor camera imaging. By observing the fact that the conventional directional weighted interpolation methods are based on unreliable assumptions using spectral correlation, a contribution of this work is made using an antialiasing finite impulse response filter to improve the interpolation accuracy by exploiting robust spectral correlation. We also make a contribution toward refining the interpolation result by using the gradient inverse weighted filtering method. An experimental analysis of images revealed that our proposed algorithm provided superior performance in terms of both objective and subjective image quality compared to conventional directional weighted demosaicking algorithms. Our implementation has very low complexity and is, therefore, well suited for real-time applications.

2 citations


Cited by
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Journal ArticleDOI
TL;DR: Simulation results verify that, the proposed framework performs better than state-of-the-art demosaicking methods in term of color peak signal-to-noise ratio (CPSNR) and feature similarity index measure (FSIM), as well as higher visual quality.
Abstract: In this letter, we proposed a new framework for color image demosaicking by using different strategies on green (G) and red/blue (R/B) components. Firstly, for G component, the missing samples are estimated by eight-direction weighted interpolation via exploiting spatial and spectral correlations of neighboring pixels. The G plane can be well reconstructed by considering the joint contribution of pre-estimations along eight interpolation directions with different weighting factors. Secondly, we estimate R/B components using guided filter with the reconstructed G plane as guidance image. Simulation results verify that, the proposed framework performs better than state-of-the-art demosaicking methods in term of color peak signal-to-noise ratio (CPSNR) and feature similarity index measure (FSIM), as well as higher visual quality.

36 citations

Journal ArticleDOI
TL;DR: A very low cost edge sensing scheme is proposed, which guides demosaicking by a logistic functional of the difference between directional variations, which achieves substantially higher accuracy and significantly lower cost.
Abstract: Digital cameras that use color filter arrays (CFA) entail a demosaicking procedure to form full RGB images. To digital camera industry, demosaicking speed is as important as demosaicking accuracy, because camera users have been accustomed to viewing captured photos instantly. Moreover, the cost associated with demosaicking should not go beyond the cost saved by using CFA. For this purpose, we revisit the classical Hamilton–Adams (HA) algorithm, which outperforms many sophisticated techniques in both speed and accuracy. Our analysis shows that the HA pipeline is highly efficient to exploit the originally captured data, but its oversimplified inter- and intra-channel smoothness formulation hinder its accuracy. Therefore, we propose a very low cost edge sensing scheme, which guides demosaicking by a logistic functional of the difference between directional variations. We extensively compare our algorithm with 27 demosaicking algorithms by running their open source code on benchmark datasets. Compared with the methods of similar computational cost, our method achieves substantially higher accuracy, whereas compared with the methods of similar accuracy, our method has significantly lower cost. On test images of currently popular resolution, the quality of our algorithm is comparable to top performers, yet our speed is tens of times faster. Source code is submitted to http://ieeexplore.ieee.org .

26 citations

Journal ArticleDOI
TL;DR: Experiments reveal that the proposed pipeline attains excellent visual quality while providing compression performance competitive to that of state-of-the-art compression algorithms for mosaic images.
Abstract: Digital cameras have become ubiquitous for amateur and professional applications. The raw images captured by digital sensors typically take the form of color filter array (CFA) mosaic images, which must be "developed" (via digital signal processing) before they can be viewed. Photographers and scientists often repeat the "development process" using different parameters to obtain images suitable for different purposes. Since the development process is generally not invertible, it is commonly desirable to store the raw (or undeveloped) mosaic images indefinitely. Uncompressed mosaic image file sizes can be more than 30 times larger than those of developed images stored in JPEG format. Thus, data compression is of interest. Several compression methods for mosaic images have been proposed in the literature. However, they all require a custom decompressor followed by development-specific software to generate a displayable image. In this paper, a novel compression pipeline that removes these requirements is proposed. Specifically, mosaic images can be losslessly recovered from the resulting compressed files, and, more significantly, images can be directly viewed (decompressed and developed) using only a JPEG 2000 compliant image viewer. Experiments reveal that the proposed pipeline attains excellent visual quality, while providing compression performance competitive to that of state-of-the-art compression algorithms for mosaic images.

25 citations

Journal ArticleDOI
01 Mar 2018
TL;DR: An improved fuzzy clustering and weighted scheme reconstruction framework that outperforms some state-of-art super-resolution methods in both quantitatively and perceptually.
Abstract: Exploring sparse representation to enhance the resolution of infrared image has attracted much attention in the last decade. However, conventional sparse representation-based super-resolution aim at learning a universal and efficient dictionary pair for image representation. However, considering that a large number of different structures exist in an image, it is insufficient and unreasonable to present various image structures with only one universal dictionary pair. In this paper, we propose an improved fuzzy clustering and weighted scheme reconstruction framework to solve this problem. Firstly, the training patches are divided into multiple clusters by joint learning multiple dictionary pairs with improved fuzzy clustering method. The goal of joint learning is to learn the multiple dictionary pairs which could collectively represent all the training patches with smallest reconstruction error. So that the learned dictionary pairs are more precise and mutually complementary. Then, high-resolution (HR) patches are estimated according to several most accurate dictionary pairs. Finally, these estimated HR patches are integrated together to generate a final HR patch by a weighted scheme. Numerous experiments demonstrate that this framework outperforms some state-of-art super-resolution methods in both quantitatively and perceptually.

24 citations

Journal ArticleDOI
TL;DR: Experimental results indicate that the proposed adaptive network-based fuzzy inference system with a sliding-level particle swarm optimization (SL-PSO) is a reliable method for optimizing CMP-CF processes.
Abstract: An adaptive network-based fuzzy inference system (ANFIS) with a sliding-level particle swarm optimization (SL-PSO) is proposed for optimizing parameters of a chemical-mechanical process for polishing a color filter (CMP-CF). The SL-PSO is used not only to find the best membership function types, but also to optimize the premise and consequent parameters for ANFIS. The important process parameters for CMP-CF included the initial time, polishing time, polishing pad weight (down force), slurry chemicals, and rotation speed. The output targets were red pixels, green pixels, blue pixels, and the surface roughness. First, the performance of the SL-PSO was tested with 18 continuous global numerical optimization problems, including six unimodal functions, seven multimodal functions, and five complex rotated and shifted functions. Nonparametric Wilcoxon tests were also used in multiple-problem analysis for simultaneous comparison of various algorithms over a problem set. The computational experiments showed that the proposed SL-PSO approach outperforms PSO-based methods reported in the literature. Finally, the proposed SL-PSO method was used to optimize CMP-CF parameters. The experimental results showed that the ANFIS with SL-PSO outperforms the conventional ANFIS method and conventional back propagation neural network in terms of prediction accuracy. A practical industrial application in a CF manufacturer showed that the ANFIS with SL-PSO obtained superior results compared to the previous method and immediately enhanced production efficiency. Together, these experimental results indicate that the proposed ANFIS with SL-PSO is a reliable method for optimizing CMP-CF processes.

19 citations