Abstract
Capturing high-quality images via mobile devices in low-light or backlighting conditions is very challenging. In this paper, a new, single image brightening algorithm is proposed to enhance an image captured in low-light conditions. Two virtual images with larger exposure times are generated to increase brightness and enhance fine details of the underexposed regions. In order to reduce the brightness change, the virtual images are generated via intensity mapping functions (IMFs) which are computed using available camera response functions (CRFs). To avoid possible color distortion in the virtual image due to one-to-many mapping, a least square minimization problem is formulated to determine brightening factors for all pixels in the underexposed regions. In addition, an edge-preserving smoothing technique is adopted to avoid noise in the underexposed regions from being amplified in the virtual images. The final brightened image is obtained by fusing the original image and two virtual images via a gradient domain guided image filtering (GGIF) based multiscale exposure fusion (MEF) with properly defined weights for all the images. Experimental results show that the relative brightness and color are preserved better by the proposed algorithm. The details in bright regions are also preserved well in the final image. The proposed algorithm is expected to be useful for computational photography on smart phones.
Highlights
Exposure time and ISO values are usually carefully tuned by professional photographers to capture well-posed images under various lighting conditions [1,2]
To get the good fused image, in this subsection, the three images are fused via a gradient domain guided image filtering (GGIF) based on multiexposure images fusion (MEF) [22]
The ground truths of enhanced image are nine real exposure images and the ground truth of virtual image is the real image with the same exposure time as virtual image
Summary
Exposure time and ISO values are usually carefully tuned by professional photographers to capture well-posed images under various lighting conditions [1,2]. This is because the true relationship of intensity from a dark image to a bright one is a one-to-many mapping for all underexposed pixels [16]. This issue is ignored by most deep-learning-based single image brightening algorithms It is desired to design a simple, single image brightening algorithm which can avoid brightness change, overexposure, and color distortion as well as noise amplification from appearing in the brightened image.
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