MICROSCALE IMAGE ENHANCEMENT VIA PCA AND WELL-EXPOSEDNESS MAPS

The restrictions of accessing high-end microscopes, microscale cameras and high-tech imaging lenses result in a high demand on low-cost microscopes. However, low-cost microscopes are facing with many image capture and quality limitations due to incompatible equipped instrumentation. This study aims at overcoming illumination and contrast problems, color aberration issues, and blur and noise corruption in low-cost microscopes at high image magnification rates. The three color channels of the input image are enhanced via principal component analysis and well-exposedness feature maps by means of cross-channel histogram matching, Laplacian and non-local means filtering. The proposed approach produces sharper, and better color and illumination fixed outputs when compared to existing methods in literature.

Automated summary

This study aims at addressing the issues of illumination, contrast, color aberration, blur, and noise in low-cost microscopes at high image magnification rates. By utilizing principal component analysis and feature map processing, the proposed method produces sharper, and better color and illumination fixed outputs.