Super-resolution image restoration for microlens array imaging system

Microlens array (MLA) has been widely applied in optical imaging fields. However, the existing MLA imaging system (MLAIS) often suffers the problems of low imaging resolutions and luminance contrast due to the limitation of the sensor size and microlens aperture. To address these problems, we propose a super-resolution (SR) image restoration method for the microlens array imaging system (MLAIS). The proposed method uses an OtsuKmeans gravity-based center extraction method to segment the image captured by MLAIS into many sub-images and an enhanced correlation coefficient-based approach to align the sub-images. A maximum a posteriori probability algorithm is developed to recover the SR image from the aligned sub-images. A two-step gray transformation algorithm is designed to enhance the SR image. Numerical simulations and practical experiments are implemented to verify the effectiveness of the proposed method, and its SR restoration performance is evaluated by comparing the results with classical and state-of-the-art methods. The results indicate that the proposed method can achieve high-quality SR image restoration for the MLAIS, and its SR performance is better than the comparison methods. The proposed method may find applications in multispectral SR imaging, remote sensing SR imaging, muti-sensor SR imaging, and so on.

Automated summary

This paper proposes a super-resolution image restoration method for microlens array imaging system (MLAIS) by segmenting the image, aligning sub-images, recovering SR image, and performing gray transformation to enhance image quality.