Robust Multi-Exposure Image Fusion: A Structural Patch Decomposition Approach

Ma, Kede; Li, Hui; Yong, Hongwei; Wang, Zhou; Meng, Deyu; Zhang, Lei

doi:10.1109/tip.2017.2671921

Cited by 279 publications

(244 citation statements)

References 53 publications

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“…Finally, by combining merged detail layer and merged base layer, the fused image is obtained. K. Ma et al [11] proposed a multi-exposure image fusion method that avoids ghosting effect. The image patch is divided into three elements called mean intensity, signal structure, and signal strength.…”

Section: E Advanced Multi-exposure Image Fusion Methodsmentioning

confidence: 99%

Image Fusion Techniques:

Subramanian¹,

Sowndariya²,

Swathi³

et al. 2020

IJRTE

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Image Fusion is a pioneering technique in the area of image processing to generate high quality images. In image fusion, more than one images of a scene with varying conditions are considered and fused together to get an image with more details than the input images. The required features and details will be enhanced in fused image. This technique is used in different areas of application for correcting and enhancing the details that are not visible in sources. This paper provides a clear idea about different Image Fusion approaches. A detailed review on advanced Multi-Exposure Image Fusion methods is also provided. A comparative study on important multi-exposure image fusion methods are done both quantitatively and qualitatively.

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Section: E Advanced Multi-exposure Image Fusion Methodsmentioning

confidence: 99%

Image Fusion Techniques:

Subramanian¹,

Sowndariya²,

Swathi³

et al. 2020

IJRTE

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“…Our method is able to detect the useful information in these two images and re-assemble them to a new image which contains all the relevant information and looks visually pleasing. We compare our method with the other two state-of-the-art approaches on multi-exposure image fusion task: the SPD-MEF [69] and MEF-OPT [70]. As we can see in Fig.…”

Section: Multi-exposure Image Fusionmentioning

confidence: 99%

Deep Convolutional Neural Network for Multi-Modal Image Restoration and Fusion

Deng

Dragotti

2021

IEEE Trans. Pattern Anal. Mach. Intell.

118

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In this paper, we propose a novel deep convolutional neural network to solve the general multi-modal image restoration (MIR) and multi-modal image fusion (MIF) problems. Different from other methods based on deep learning, our network architecture is designed by drawing inspirations from a new proposed multi-modal convolutional sparse coding (MCSC) model. The key feature of the proposed network is that it can automatically split the common information shared among different modalities, from the unique information that belongs to each single modality, and is therefore denoted with CU-Net, i.e., Common and Unique information splitting network. Specifically, the CU-Net is composed of three modules, i.e., the unique feature extraction module (UFEM), common feature preservation module (CFPM), and image reconstruction module (IRM). The architecture of each module is derived from the corresponding part in the MCSC model, which consists of several learned convolutional sparse coding (LCSC) blocks. Extensive numerical results verify the effectiveness of our method on a variety of MIR and MIF tasks, including RGB guided depth image super-resolution, flash guided non-flash image denoising, multi-focus and multi-exposure image fusion. Index Terms-Multi-modal image restoration, image fusion, multi-modal convolutional sparse coding ! • The authors are with the

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“…Over the past decades, many powerful approaches have been developed to produce still HDR images from sequences with different exposures [DM97, SKY∗12, HGPS13, OLTK15, MLY∗17, KR17, WXTT18], burst images [LYT∗14, HSG∗16], or a single LDR image [EKD∗17, EKM17, MBRHD18]. However, most of these approaches only demonstrate results for generating still HDR images and are not suitable for producing HDR videos [KSB∗13].…”

Section: Related Workmentioning

confidence: 99%

Deep HDR Video from Sequences with Alternating Exposures

Kalantari¹,

Ramamoorthi

2019

Computer Graphics Forum

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A practical way to generate a high dynamic range (HDR) video using off‐the‐shelf cameras is to capture a sequence with alternating exposures and reconstruct the missing content at each frame. Unfortunately, existing approaches are typically slow and are not able to handle challenging cases. In this paper, we propose a learning‐based approach to address this difficult problem. To do this, we use two sequential convolutional neural networks (CNN) to model the entire HDR video reconstruction process. In the first step, we align the neighboring frames to the current frame by estimating the flows between them using a network, which is specifically designed for this application. We then combine the aligned and current images using another CNN to produce the final HDR frame. We perform an end‐to‐end training by minimizing the error between the reconstructed and ground truth HDR images on a set of training scenes. We produce our training data synthetically from existing HDR video datasets and simulate the imperfections of standard digital cameras using a simple approach. Experimental results demonstrate that our approach produces high‐quality HDR videos and is an order of magnitude faster than the state‐of‐the‐art techniques for sequences with two and three alternating exposures.

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Robust Multi-Exposure Image Fusion: A Structural Patch Decomposition Approach

Cited by 279 publications

References 53 publications

Image Fusion Techniques:

Image Fusion Techniques:

Deep Convolutional Neural Network for Multi-Modal Image Restoration and Fusion

Deep HDR Video from Sequences with Alternating Exposures

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