Article Details

Multi-Exposure Image Fusion Based on Illumination Estimation | Original Article

Ankita Pandey*, Soni Changlani, in Journal of Advances and Scholarly Researches in Allied Education | Multidisciplinary Academic Research

ABSTRACT:

High dynamic range (HDR) photos with varying exposures. Pixels in the original picture sequence are analyzed to determine how well exposed they are using illumination estimate filtering. Following these baseline estimates, membership functions are used to give certain pixels in the picture sequence more or lesser importance. Imaging a diatom species with complex silica-based cell walls and multi-scale patterns with a conventional microscope is insufficient due to the limited dynamic range of the data collected. Microscopy methods that include taking many photos with varying exposures in order to glean features from the diatom are used. A new breakthrough indicates that image fusion overcomes the limits of typical digital cameras to capture details from high dynamic range scene or specimen taken utilizing microscopic imaging methods. Visual evaluation and numerical metrics of contrast, brightness, and saturation are used to assess the effectiveness of the suggested strategy. The results demonstrate the method's effectiveness in boosting details without altering the color balance or adding saturation artifacts, and also highlight the value of fusion approaches for picture improvement purposes. Furthermore, we develop an encoder that combines a CNN module with a transformer module to make up for the shortcoming in building long-range dependencies in CNN-based architectures. This combination allows the network to concentrate on both local and global information. On the most recent iteration of the publicly available multi-exposure picture fusion benchmark dataset, we found that our technique outperformed its rival conventional and deep learning-based counterparts in both subjective and objective assessments.