Abstract

Objective Color images are typically processed using the Bayer pattern in the color filter array （CFA） during image acquisition. However， this approach often generates a band of artifact pixels around image edges， causing colors deviation from the actual objects. To address this issue， this paper proposes a method for identifying and removing edge artifact pixels in color images， thereby improving the accuracy of color representation for real-world objects and laying the foundation for future international standards.

Methods A simulation program for generating color images was constructed in Matlab software. Opaque black simulated particles were used as the research object to mimic the conversion process from raw （RAW） to RGB （red， green and blue channels） format images under the Bayer filter within the CFA. A color image acquisition setup was established to capture images of a dot calibration board， and the actual size and color of the black dots were calibrated to investigate the formation mechanism of edge artifacts in color images. A combined approach of simulations and experiments was adopted to examine the impact of 6 demosaicing interpolation algorithms and lens chromatic aberration on the number of artifact pixels in color images. To validate this method， red and blue polystyrene particles and yellow and white paracetamol-caffeine-artificial cow-bezoar-chlorphenamine maleate granules were used as application subjects. Color images of these particles were captured， and changes in color momentsand chromaticity coordinates before and after artifact removal were analyzed. It demonstrated the method’s applicability and universality across different particle types.

Results and Discussion In simulated color images， the 6 demosaicing interpolation algorithms generated ftom 2 to 3 edge artifact pixels. The number of edge artifact pixels in the captured images of black dots exceeded 3. These 6 interpolation algorithms exhibited different degrees of influence on the mean， maximum， and modal values of color differences in the converted RGB color images. When 5 edge artifact pixels were removed， the standard deviations of the R， G， and B channel values in the black dot images all fell below 0.01， approaching background noise levels. The removal of artifact pixels effectively removed the artifacts， and color fluctuations were minimized. Upon the removal of 5 edge artifact pixels， the first-order moments in the color moments of the particle images for both polystyrene and paracetamol-caffeine-artificial cow-bezoar-chlorphenamine maleate granules increased. However， the second and third-order moments decreased. The distribution area of particle pixels in the chromaticity coordinate diagrams was significantly reduced， and the clustering of coordinate points was enhanced. These results accurately reflected the true colors of the particles.

Conclusion The edge artifact pixel identification and removal methods effectively reduce statistical color differences in color images. The overall luminance of particle images is closer to their true values， the color fluctuation is decreased， and the balance of light and dark tones is achieved. The dual goals of color fidelity and morphological fidelity are achieved.

Keywords： color image； edge artifact； interpolation algorithm； color difference； color moment； chromaticity coordinate

Get Citation: HAN Xue， ZHOU Wu， CAI Tianyi， et al. Identification and removal methods of edge artifacts in color images［J］. China Powder Science and Technology， 2026， 32（2）： 1-16.

Received: 2024-12-30 .Revised: 2025-04-26,Online: 2025-09-17.

Funding Project: 国家自然科学基金项目， 编号： 52376163； 上海市科学技术委员会启明星培育（扬帆）计划项目， 编号： 22YF1429600。

First Author: 韩雪（1998—），女，硕士生，研究方向为彩色图像的颗粒测量。E-mail：hx_0117@163.com。

Corresponding Author: 周骛（1986—），女，教授，博士，博士生导师，研究方向为颗粒与两相流测量。E-mail：zhouwu@usst.edu.cn。

DOI：10.13732/j.issn.1008-5548.2026.02.012

CLC No:TP751.1； TB44                  Type Code: A

Serial No: 1008-5548（2026）02-0001-16