Objective

2

Good quality images with rich information and good visual effect have been concerned in digital imaging. Due to the limitation of "dynamic range"

existing imaging equipment cannot record all the details of one scene via one exposure imaging

which seriously affects the visual effect and key information retention of source images. A mismatch in the dynamic range has been caused to real scene amongst existing imaging

display equipment and human eye's response. The dynamic range can be regarded as the brightness ratio between the brightest and darkest points in natural scene ima-ges. The dynamic range of human visual system is 10

5

: 1. The dynamic range of images captured/displayed by digital imaging/display equipment is only 10

2

: 1

which is significantly lower than the corresponding one of human visual system. Multi-exposure image fusion has provided a simple and effective way to solve the mismatch of dynamic range between existing imaging/display equipment and human eye. Multi-exposure image fusion has been used to perform the weighted fusion of multiple images via various cameras at different exposure levels. The information of source images has been maximally retained in the fused images

which ensures the fused images have the high-dynamic-range visual effect that matches the resolution of human eyes.

Method

2

Multi-exposure image fusion methods have usually categorized into spatial domain methods and transform domain methods. Spatial domain fusion methods either first divide source image sequences into image blocks according to certain rules and then perform the fusion of image blocks

or directly do the pixel-based fusion. The fused images often have different problems

such as unnatural connection of transition areas and uneven brightness distribution

which causes the low structural similarity between the fused images and source images. Transform domain fusion methods first decompose source images into the transform domain

and then perform the fusion according to certain fusion rules. Image decomposition has mainly divided into multi-scale decomposition and two-scale decomposition. Multi-scale decomposition requires up-sampling and down-sampling operations

which cause a certain degree of image information loss. Two-scale decomposition does not contain up-sampling and down-sampling operations

which avoids the problem of information loss caused by multi-scale decomposition

and avoids the shortcomings of spatial domain method to a certain extent. Two-scale decomposition can be directly decomposed into base and detail layers using filters

but the selection of filters seriously affects the quality of the fused images. A new exposure fusion algorithm based on two-scale decomposition and color prior has been proposed to obtain visual effect images like high-quality HDR (high dynamic range) images. The details of overexposed area and dark area can be involved in and the fused image has good color saturation. The main contributions have shown as follows: 1) To use the difference between image brightness and image saturation to determine the degree of exposure; To combine the difference and the image contrast as a quality measure. This method can distinguish the overexposed area and the unexposed area quickly and efficiently. The texture details of the overexposed area and the unexposed area have been considered. The color saturation and contrast of the fused image have been improved. 2) A fusion method based on two-scale decomposition has been presented. Fast guided filter to decompose the image can reduce the halo artifacts of the fused image to a certain extent

which makes the image have better visual effect. The detailed research workflows have shown as follows: First

the image has been decomposed by fast guided filtering. The obtained detail layer has been enhanced

which retains more detailed information

and reduces the halo artifacts of the fused image. Next

based on the color prior

the difference between brightness and saturation has been used to determine the degree of image exposure. The difference and the image contrast have been combined to calculate the fusion weight of multi-exposure images

ensuring the brightness and contrast of the fused images at the same time. At last

the guided filtering has been used to optimize the weight map

suppress noise

increase the pixels correlation and improve the visual effect of the fused images.

Result

2

24 sets of multi-exposure image sequences have been included. From the overall contrast and color saturation of the fused images and the details of both overexposed and underexposed areas have been improved on the perspective of subjective evaluation. In terms of the analysis of objective evaluation criteria

two different quality evaluation algorithms have used to evaluate the fused results of multi-exposure image sequences. The evaluation results have shown that the averages of corresponding indicators reach 0.982 and 0.970 each. The two different structural similarity indexes have been improved

with an average improvement of 1.2% and 1.1% respectively.

Conclusion

2

According to subjective and objective evaluations

the good fusion performance of significant processing effects on image contrast

color saturation and detail information retention has been outreached. The algorithm has three main advantages with respect to low complexity

simple implementation and relatively fast running speed

to customize mobile devices. It can be applied to imaging equipment with low dynamic range to obtain ideal images.