Recursive HDR image generation from differently exposed images based on local image properties

Dynamic range limitation of CCD-cameras may cause distortions and data loses in images. Such limitations are strongly effect to the further image processing. This paper describes method of combining information from differently exposed images for increasing dynamic range. Initially image is decomposed into set of regions. For each of region we compute detail evaluation function which represents its local properties. Namely mean intensity, intensity deviation and entropy. This function is used to detect regions with high dynamic range. The regions with high dynamic range are then recursively decomposed. This process iterates until all HDR regions are processed, or the size of these regions is too small for decomposition. During the process of decomposition we select the best exposure for each sub-region. For smoothing interregional transaction we used Gaussian-based smoothing function. Proposed technique allows recovering details in overexposed and underexposed parts of image. Our experiments show effectiveness of algorithm for the scenes with high dynamic range. Proposed method shows robust results even if the exposure difference between input images is 2-stops or higher.