Detail Enhanced Multi-Exposure Image Fusion Based On Edge Preserving Filters

Abstract

Recent computational photography techniques play a significant role to overcome the limitation of standard digital cameras for handling wide dynamic range of real-world scenes contain brightly and poorly illuminated areas. In many of such techniques [1,2,3], it is often desirable to fuse details from images captured at different exposure settings, while avoiding visual artifacts. One such technique is High Dynamic Range (HDR) imaging that provides a solution to recover radiance maps from photographs taken with conventional imaging equipment. The process of HDR image composition needs the knowledge of exposure times and Camera Response Function (CRF), which is required to linearize the image data before combining Low Dynamic Range (LDR) exposures into HDR image. One of the long-standing challenges in HDR imaging technology is the limited Dynamic Range (DR) of conventional display devices and printing technology. Due to which these devices are unable to reproduce full DR. Although DR can be reduced by using a tone-mapping, but this comes at an unavoidable trade-off with increased computational cost. Therefore, it is desirable to maximize information content of the synthesized scene from a set of multi-exposure images without computing HDR radiance map and tone-mapping.This research attempts to develop a novel detail enhanced multi-exposure image fusion approach based on texture features, which exploits the edge preserving and intra-region smoothing property of nonlinear diffusion filters based on Partial Differential Equations (PDE). With the captured multi-exposure image series, we first decompose images into Base Layers (BLs) and Detail Layers (DLs) 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. In the next step texture features of the BL to generate a decision mask (i.e., local range) have been considered that guide the fusion of BLs in multi-resolution fashion. Finally, well-exposed fused image is obtained that combines fused BL and the DL at each scale across all the input exposures. The combination of edge-preserving filters with Laplacian pyramid is shown to lead to texture detail enhancement in the fused image.Furthermore, Non-linear adaptive filter is employed for BL and DL decomposition that has better response near strong edges. The texture details are then added to the fused BL to reconstruct a detail enhanced LDR version of the image. This leads to an increased robustness of the texture details while at the same time avoiding gradient reversal artifacts near strong edges that may appear in fused image after DL enhancement.Finally, we propose a novel technique for exposure fusion in which Weighted Least Squares (WLS) optimization framework is utilized for weight map refinement of BLs and DLs, which lead to a new simple weighted average fusion framework. Computationally simple texture features (i.e. DL) and color saturation measure are preferred for quickly generating weight maps to control the contribution from an input set of multi-exposure images. Instead of employing intermediate HDR reconstruction and tone-mapping steps, well-exposed fused image is generated for displaying on conventional display devices. Simulation results are compared with a number of existing single resolution and multi-resolution techniques to show the benefits of the proposed scheme for the variety of cases. Moreover, the approaches proposed in this thesis are effective for blending flash and no-flash image pair, and multi-focus images, that is, input images photographed with and without flash, and images focused on different targets, respectively. A further advantage of the present technique is that it is well suited for detail enhancement in the fused image.