Low-Light Image Brightening via Fusing Additional Virtual Images

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.

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Weighted Least Squares Based Detail Enhanced Exposure Fusion

ISRN Signal Processing ◽

10.1155/2014/498762 ◽

2014 ◽

Vol 2014 ◽

pp. 1-18 ◽

Cited By ~ 6

Author(s):

Harbinder Singh ◽

Vinay Kumar ◽

Sunil Bhooshan

Keyword(s):

Least Squares ◽

Dynamic Range ◽

Weighted Least Squares ◽

Texture Features ◽

High Dynamic Range ◽

Computational Photography ◽

Edge Preserving ◽

Exposure Fusion ◽

Fused Image ◽

Visual Artifacts

Many recent computational photography techniques play a significant role to avoid limitation of standard digital cameras to handle wide dynamic range of the real-world scenes, containing brightly and poorly illuminated areas. In many of these techniques, it is often desirable to fuse details from images captured at different exposure settings, while avoiding visual artifacts. In this paper we propose a novel technique for exposure fusion in which Weighted Least Squares (WLS) optimization framework is utilized for weight map refinement. Computationally simple texture features (i.e., detail layer extracted with the help of edge preserving filter) and color saturation measure are preferred for quickly generating weight maps to control the contribution from an input set of multiexposure images. Instead of employing intermediate High Dynamic Range (HDR) reconstruction and tone mapping steps, well-exposed fused image is generated for displaying on conventional display devices. A further advantage of the present technique is that it is well suited for multifocus image fusion. Simulation results are compared with a number of existing single resolution and multiresolution techniques to show the benefits of the proposed scheme for variety of cases.

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Multi-scale exposure fusion via gradient domain guided image filtering

2017 IEEE International Conference on Multimedia and Expo (ICME) ◽

10.1109/icme.2017.8019529 ◽

2017 ◽

Cited By ~ 20

Author(s):

Fei Kou ◽

Zhengguo Li ◽

Changyun Wen ◽

Weihai Chen

Keyword(s):

Image Filtering ◽

Exposure Fusion ◽

Multi Scale ◽

Gradient Domain ◽

Guided Image Filtering

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Low-Light Image Enhancement Based on Guided Image Filtering in Gradient Domain

International Journal of Digital Multimedia Broadcasting ◽

10.1155/2017/9029315 ◽

2017 ◽

Vol 2017 ◽

pp. 1-13 ◽

Cited By ~ 3

Author(s):

Xiankun Sun ◽

Huijie Liu ◽

Shiqian Wu ◽

Zhijun Fang ◽

Chengfan Li ◽

...

Keyword(s):

Image Enhancement ◽

Objective Evaluation ◽

Low Light ◽

Edge Preserving ◽

Light Image ◽

Guided Image Filter ◽

Reflection Model ◽

Novel Approach ◽

Gradient Domain ◽

Reflection Component

We propose a novel approach for low-light image enhancement. Based on illumination-reflection model, the guided image filter is employed to extract the illumination component of the underlying image. Afterwards, we obtain the reflection component and enhance it by nonlinear functions, sigmoid and gamma, respectively. We use the first-order edge-aware constraint in the gradient domain to achieve good edge preserving features of enhanced images and to eliminate halo artefact effectively. Moreover, the resulting images have high contrast and ample details due to the enhanced illumination and reflection component. We evaluate our method by operating on a large amount of low-light images, with comparison with other popular methods. The experimental results show that our approach outperforms the others in terms of visual perception and objective evaluation.

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