Multidimensional Systems and Signal Processing https://doi.org/10.1007/s11045-019-00655-6 Fusion of multi-exposure images using recursive and Gaussian filter Vishal Chaudhary 1 · Vinay Kumar 1 Received: 5 March 2018 / Revised: 7 May 2019 / Accepted: 10 May 2019 © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract This paper proposes a novel technique to create a high-resolution image by combining the bracketed exposure sequence without a priori knowledge of source image. The source image is split into three categories: constant, high varying and low varying feature images. For high and low varying features, pixels with highest information is selected and combined to construct collective high and low varying feature image. Collective constant feature image is constructed from weighted average of constant feature images, where weight is calculated based on information present in original source images. These pre-processed high, low and constant feature images are further combined to produce a final fused image. Objective anal- ysis based quality evaluation parameters show a significant improvement in result produced by proposed method against the state-of-the-art. Keywords Dynamic range · Exposure time · Recursive filter · Image fusion 1 Introduction A scene varies largely by exposure level, therefore a single LDR (low dynamic range) camera can never collect all underlying informations of the scene. The captured image may experience under or over-exposed areas due to high or low shutter speed of the camera, respectively. High shutter speed or low exposure time results in information loss in darker region and vice versa (refer Fig. 1). An image constructed by collecting complementary information from varying exposure time LDR images (source images) can preserve all the details of the scene. Two major approaches to perform the operation are: HDR (high dynamic range) tone mapping and multi-exposure image fusion. LDR source images are combined together to construct HDR tone mapped image by extracting Camera Response Function (CRF). Extraction of CRF requires additional information of source images; like, exposure time and exposure value. Next, dynamic range of resulting HDR image is compressed to LDR while preserving B Vishal Chaudhary vishalch.nsit@gmail.com 1 Department of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala, India 123