Multiple exposure image intensifying method

Abstract

The method comprises: shooting multi pictures with different exposure levels at same scene; according to the response curve of camera, portioning the each picture into several blocks; selecting the blocks having the most information to make block combination; using a fusion function to remove the edge effects between blocks to finally get an enhanced picture with wide dynamical range.

Description

technical field [0001] The present invention relates to a method in the technical field of image processing, in particular to a method for enhancing a multi-exposure image, that is, a method for enhancing the dynamic range of light and shade by using multiple images of the same scene with different exposure degrees obtained through multi-exposure. Background technique [0002] The actual scene that can be observed by the human eye has a very large dynamic range of light and dark, and the detailed information of light intensity is very rich. Constrained by charge-coupled devices and analog-to-digital converters, digital cameras have a limited dynamic range and can only record a limited range of contrast, brightness, and number of colors. When the dynamic range of the shooting scene exceeds the capture range of the camera, no matter how the camera exposure is adjusted, there will always be overexposed or underexposed areas in the final image, resulting in loss of details in hi...

AI Technical Summary

Problems solved by technology

A class of methods, such as "Recovering high dynamic range radiance maps from photographs" (from photos In this paper, a high dynamic range tone mapping method is proposed. The specific method is to use multiple images of the same scene with different exposures to obtain high dynamic range images, and then perform tone mapping to obtain a picture with rich light and dark details. The disadvantage is that the calculation complexity is high, and it is easy to introduce details that do not exist in the source image in tone mapping, and it is seriously affected by halo and noise.

Another class of methods such as "Fusion of multi-exposure images" (multi-exposure image fusion) published by Ardeshir Goshtasby in "Image and Vision Computing" (Image and Visual Computing) 2005, Volume 23, No. 6, pp. 611-618. A method for direct fusion of multi-exposure images is proposed. The specific method is to evaluate the image information of the source image and select the pixel block with the richest information in the source image for fusion. The disadvantage lies in the existing evaluation criteria, such as information Entropy, mean square error, etc. are all based on statistical methods, which objectively evaluate some characteristics of the image, and do not take into account factors such as scene content, image bearing media, and human visual characteristics, nor make good use of them. The characteristics of multi-exposure can easily lead to unsatisfactory judgment results, which are somewhat different from visual perception

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