Developing learning based intelligent fusion for deblurring confocal microscopic images

The demand of high quality confocal microscopic images is increasing for critical tasks such as study of living tissues at cellular resolution and disease diagnosis. The results of such tasks are often affected by the blur introduced in microscopic images. Removal of blur from multi-focus microscopic images without deteriorating their visual quality is a challenging task. The confocal microscopic images are obtained by averaging their frames. This process introduces the blurring artefacts that degrade the quality of microscopic images. In this work, we presented learning-based intelligent fusion to minimize the blurring artefacts of confocal microscopic images. The quality of these images is improved using the proposed individual and ensemble fusion models. In the proposed scheme, block-based features are extracted from the blurred images. These informative features are then used to develop the individual models that construct the fused images using their fusion maps. The predicted information of the individual fusion maps is then combined to construct the ensemble-based fused image. The proposed learning-based approach has demonstrated improved quantitative and qualitative results compared to rule-based fusion approaches. The proposed fusion models can be employed as a useful tool in confocal microscopy frames to generate the improved quality images with reduced blurring artefacts.