scholarly journals Weighted Least Squares Based Detail Enhanced Exposure Fusion

2014 ◽  
Vol 2014 ◽  
pp. 1-18 ◽  
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.

scholarly journals Anisotropic Diffusion for Details Enhancement in Multiexposure Image Fusion

2013 ◽  
Vol 2013 ◽  
pp. 1-18 ◽  
Author(s):  
Harbinder Singh ◽  
Vinay Kumar ◽  
Sunil Bhooshan
Keyword(s):  
Image Fusion ◽  
Dynamic Range ◽  
Texture Features ◽  
High Dynamic Range ◽  
Base Layer ◽  
Range Image ◽  
Edge Preserving ◽  
Display Devices ◽  
Fused Image ◽  
Homogeneous Regions

We develop a multiexposure image fusion method based on texture features, which exploits the edge preserving and intraregion smoothing property of nonlinear diffusion filters based on partial differential equations (PDE). With the captured multiexposure image series, we first decompose images into base layers and detail layers to extract sharp details and fine details, respectively. The magnitude of the gradient of the image intensity is utilized to encourage smoothness at homogeneous regions in preference to inhomogeneous regions. Then, we have considered texture features of the base layer to generate a mask (i.e., decision mask) that guides the fusion of base layers in multiresolution fashion. Finally, well-exposed fused image is obtained that combines fused base layer and the detail layers at each scale across all the input exposures. Proposed algorithm skipping complex High Dynamic Range Image (HDRI) generation and tone mapping steps to produce detail preserving image for display on standard dynamic range display devices. Moreover, our technique is effective for blending flash/no-flash image pair and multifocus images, that is, images focused on different targets.

scholarly journals A Novel Approach for Detail-Enhanced Exposure Fusion Using Guided Filter

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Harbinder Singh ◽  
Vinay Kumar ◽  
Sunil Bhooshan
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Guided Filter ◽  
Base Layer ◽  
Range Image ◽  
Exposure Fusion ◽  
Novel Approach ◽  
Fused Image ◽  
High Dynamic ◽  
Fusion Approach

In this paper we propose a novel detail-enhancing exposure fusion approach using nonlinear translation-variant filter (NTF). With the captured Standard Dynamic Range (SDR) images under different exposure settings, first the fine details are extracted based on guided filter. Next, the base layers (i.e., images obtained from NTF) across all input images are fused using multiresolution pyramid. Exposure, contrast, and saturation measures are considered to generate a mask that guides the fusion process of the base layers. Finally, the fused base layer is combined with the extracted fine details to obtain detail-enhanced fused image. The goal is to preserve details in both very dark and extremely bright regions without High Dynamic Range Image (HDRI) representation and tone mapping step. Moreover, we have demonstrated that the proposed method is also suitable for the multifocus image fusion without introducing artifacts.

scholarly journals High-dynamic-range imaging for cloud segmentation

2018 ◽  
Vol 11 (4) ◽  
pp. 2041-2049 ◽  
Author(s):  
Soumyabrata Dev ◽  
Florian M. Savoy ◽  
Yee Hui Lee ◽  
Stefan Winkler
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Wide Field ◽  
Single Shot ◽  
Generation Process ◽  
Image Generation ◽  
Large Dynamic Range ◽  
Exposure Fusion ◽  
Wide Field Of View ◽  
High Dynamic

Abstract. Sky–cloud images obtained from ground-based sky cameras are usually captured using a fisheye lens with a wide field of view. However, the sky exhibits a large dynamic range in terms of luminance, more than a conventional camera can capture. It is thus difficult to capture the details of an entire scene with a regular camera in a single shot. In most cases, the circumsolar region is overexposed, and the regions near the horizon are underexposed. This renders cloud segmentation for such images difficult. In this paper, we propose HDRCloudSeg – an effective method for cloud segmentation using high-dynamic-range (HDR) imaging based on multi-exposure fusion. We describe the HDR image generation process and release a new database to the community for benchmarking. Our proposed approach is the first using HDR radiance maps for cloud segmentation and achieves very good results.

scholarly journals High-Dynamic-Range Imaging for Cloud Segmentation

2017 ◽  
Author(s):  
Soumyabrata Dev ◽  
Florian M. Savoy ◽  
Yee Hui Lee ◽  
Stefan Winkler
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Wide Field ◽  
Single Shot ◽  
Generation Process ◽  
Large Dynamic Range ◽  
Exposure Fusion ◽  
Wide Field Of View ◽  
High Dynamic ◽  
Fish Eye

Abstract. Sky/cloud images obtained from ground-based sky-cameras are usually captured using a fish-eye lens with a wide field of view. However, the sky exhibits a large dynamic range in terms of luminance, more than a conventional camera can capture. It is thus difficult to capture the details of an entire scene with a regular camera in a single shot. In most cases, the circumsolar region is over-exposed, and the regions near the horizon are under-exposed. This renders cloud segmentation for such images difficult. In this paper, we propose HDRSeg – an effective method for cloud segmentation using High-Dynamic-Range (HDR) imaging based on multi-exposure fusion. We describe the HDR generation process and release a new database to the community for benchmarking. Our proposed approach is the first using HDR images for cloud segmentation and achieves very good results.

scholarly journals Two-Exposure Image Fusion Based on Optimized Adaptive Gamma Correction

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 24
Author(s):  
Yan-Tsung Peng ◽  
He-Hao Liao ◽  
Ching-Fu Chen
Keyword(s):  
Image Fusion ◽  
Dynamic Range ◽  
State Of The Art ◽  
High Dynamic Range ◽  
Gamma Correction ◽  
Virtual Image ◽  
Exposure Fusion ◽  
Multi Scale ◽  
Fusion Methods ◽  
Adaptive Gamma Correction

In contrast to conventional digital images, high-dynamic-range (HDR) images have a broader range of intensity between the darkest and brightest regions to capture more details in a scene. Such images are produced by fusing images with different exposure values (EVs) for the same scene. Most existing multi-scale exposure fusion (MEF) algorithms assume that the input images are multi-exposed with small EV intervals. However, thanks to emerging spatially multiplexed exposure technology that can capture an image pair of short and long exposure simultaneously, it is essential to deal with two-exposure image fusion. To bring out more well-exposed contents, we generate a more helpful intermediate virtual image for fusion using the proposed Optimized Adaptive Gamma Correction (OAGC) to have better contrast, saturation, and well-exposedness. Fusing the input images with the enhanced virtual image works well even though both inputs are underexposed or overexposed, which other state-of-the-art fusion methods could not handle. The experimental results show that our method performs favorably against other state-of-the-art image fusion methods in generating high-quality fusion results.

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