Abstract
Multi exposure image fusion (MEF) provides a concise way to generate high-dynamic-range (HDR) images. Although the precise fusion can be achieved by existing MEF methods in different static scenes, the corresponding performance of ghost removal varies in different dynamic scenes. This paper proposes a precise MEF method based on feature patches (FPM) to improve the robustness of ghost removal in a dynamic scene. A reference image is selected by a priori exposure quality first and then used in the structure consistency test to solve the image ghosting issues existing in the dynamic scene MEF. Source images are decomposed into spatial-domain structures by a guided filter. Both the base and detail layer of the decomposed images are fused to achieve the MEF. The structure decomposition of the image patch and the appropriate exposure evaluation are integrated into the proposed solution. Both global and local exposures are optimized to improve the fusion performance. Compared with six existing MEF methods, the proposed FPM not only improves the robustness of ghost removal in a dynamic scene, but also performs well in color saturation, image sharpness, and local detail processing.
Highlights
Existing imaging devices cannot capture all the details in a scene by a one-time exposure
For both color and brightness, the image captured by an imaging device is different from the one observed by human eyes in a real scene, such as an image captured in a night scene
Existing Multi exposure image fusion (MEF) solutions are mainly applicable to various static scenes, which can be categorized into transform- and spatial-domain solutions
Summary
Existing imaging devices cannot capture all the details in a scene by a one-time exposure. An image registration algorithm based on the a priori exposure quality is proposed It minimizes the local exposure distortion of the fused image caused by the improper selection of the reference image to improve the robustness of MEF in a dynamic scene. Both the structure consistency test and connectivity test are introduced to identify the ghost regions in the ghost removal process. The remaining sections of this paper are structured as follows: Section 2 discusses existing solutions of both image fusion in a static scene and ghost removal in a dynamic scene; Section 3 proposes an accurate and fast fusion framework based on the low-level image features; Section 4 compares and analyzes the experiment results; and Section 5 concludes this paper
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
