Low illumination panoramic image enhancement algorithm based on simulated multi-exposure fusion

Panoramic images captured under low-illumination conditions suffer from low contrast and poor visual effects. To address these problems, we propose a low-illumination panoramic image enhancement algorithm based on simulated multi-exposure fusion. First, the original image is converted to HSV color space; then, the optimal exposure rate is estimated by using a metric of image information entropy, and the V component is enhanced by using an intensity transform function to obtain an overexposed image. Second, a medium-exposure image is generated by using an exposure interpolation method, which utilizes the low-light image and overexposed image as input. Third, the fused image is obtained by employing a multi-fusion strategy in which the original low-illumination image, medium-exposure image, and overexposed image are fused. Finally, the detailed information is enhanced by using a multi-scale detail boosting method. The proposed method exhibits better performance compared with NPE, LIME, SRIE, Li, Ying, and RtinexNet algorithms. In case of panoramic images of different scenes, the lightness order error is 322, natural image quality evaluator is 2.32, blind/referenceless image spatial quality evaluator is 5.71, and structure similarity index is 0.82. The comprehensive performance of the proposed method is found to be better than that of other comparison algorithms. Experimental results show that the quality of the low-illumination panoramic image can be improved effectively by using the proposed algorithm. © 2021, Science Press. All right reserved.