Bio: Gwanggil Jeon is an academic researcher. The author has contributed to research in topics: Stairstep interpolation & Interpolation. The author has an hindex of 1, co-authored 1 publications receiving 36 citations.
01 Jan 2015
TL;DR: This paper presents a novel multidirectional weighted interpolation algorithm for color filter array interpolation that 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 inter- polation. 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 perfor- mance. Second, we propose an efficient postprocessing method to reduce interpolation artifacts based on the color difference planes. Compared with conventional state-of-the-art demosaick- ing 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.
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.
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 .
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.
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.
TL;DR: The proposed algorithm uses the channel information from several correlated neighboring pixels to reconstruct the missing color channels of each pixel and has a simple computation structure; therefore, it is appropriate for real-time hardware implementation and can be used in many real- time applications.
Abstract: An efficient edge-based technique for color filter array demosaicking is presented in this paper The proposed algorithm uses the channel information from several correlated neighboring pixels to reconstruct the missing color channels of each pixel We employ a simple edge detector to recognize the edge direction of each processing channel by using directional color differences, and an efficient color interpolator to reconstruct the missing color channels by observing the color correlation and edge information The proposed technique can prevent image blur and demosaicking artefacts; moreover, it has a fixed local window size and requires no previous training and no iterations Extensive experimental results demonstrate that the proposed technique preserves edge features and performs effectively in quantitative evaluations and visual quality The proposed algorithm has a simple computation structure; therefore, it is appropriate for real-time hardware implementation and can be used in many real-time applications