Saturation-Based Multi-Exposure Image Fusion Employing Local Color Correction

Multi-Exposure Fusion Method for Space Target Images Based on Multi-Feature Weights

“…( 7), L is the initial brightness of the fused image, L' is the brightness after performing local brightness correction on L. The brightness conversion ratio is calculated using Eq. (7). Then, in Eq.…”

“…According to the characteristics of space images, this paper improves the Laplacian image fusion pyramid model proposed by Mertens et al: Adaptive optimal brightness, contrast features and salient features are used as the basic weights of image fusion weights, and the final weight is obtained from these three weights. The image fusion weight of the pyramid model is used to fuse and then the local color correction technology [7] is used to improve the shortcomings of the space image.…”

Multi-Exposure Image Fusion Algorithm Based on Improved Weight Function

“…Ma et al ( 2017 ) proposed a method based on image structure block decomposition, which represents the image block with average intensity, signal intensity and signal structure, and then uses the intensity and exposure factor of the image block for weighted fusion, which can be used for both static scene fusion and dynamic scene fusion. Moriyama et al ( 2019 ) proposed to use the light conversion method of preserving hue and saturation to generate a new multi exposure image for fusion, realize brightness conversion based on local color correction, and obtain the fused image by weighted average (weight is calculated by saturation). Wang and Zhao ( 2020 ) proposed using the super-pixel segmentation method to divide the input image into non overlapping image blocks composed of pixels with similar visual attributes, decompose the image block into three independent components: signal intensity, image structure and intensity, and then fuse the three components respectively according to the characteristics of human visual system and the exposure level of the input image.…”