Multi-exposure Image Fusion Method Research Articles

An ultra-high-definition multi-exposure image fusion method based on multi-scale feature extraction

Multiple exposure image fusion is a technique used to obtain high dynamic range images. Due to its low cost and high efficiency, it has received a lot of attention from researchers in recent years. Currently, most deep learning-based multiple exposure image fusion methods extract features from different exposure images using a single feature extraction method. Some methods simply rely on two different modules to directly extract features. However, this approach inevitably leads to the loss of some feature information during the feature extraction process, thus further affecting the performance of the model. To minimize the loss of feature information as much as possible, we propose an ultra-high-definition (UHD) multiple exposure image fusion method based on multi-scale feature extraction. The method adopts a U-shaped structure to construct the overall network model, which can fully exploit the feature information at different levels. Additionally, we construct a novel hybrid stacking paradigm to combine convolutional neural networks and Transformer modules. This combined module can extract both local texture features and global color features simultaneously. To more efficiently fuse and extract features, we also design a cross-layer feature fusion module, which can adaptively learn the correlation between features at different layers. Numerous quantitative and qualitative results demonstrate that our proposed method performs well in UHD multiple exposure image fusion.

Bayesian multi-exposure image fusion for robust high dynamic range ptychography.

The limited dynamic range of the detector can impede coherent diffractive imaging (CDI) schemes from achieving diffraction-limited resolution. To overcome this limitation, a straightforward approach is to utilize high dynamic range (HDR) imaging through multi-exposure image fusion (MEF). This method involves capturing measurements at different exposure times, spanning from under to overexposure and fusing them into a single HDR image. The conventional MEF technique in ptychography typically involves subtracting the background noise, ignoring the saturated pixels and then merging the acquisitions. However, this approach is inadequate under conditions of low signal-to-noise ratio (SNR). Additionally, variations in illumination intensity significantly affect the phase retrieval process. To address these issues, we propose a Bayesian MEF modeling approach based on a modified Poisson distribution that takes the background and saturation into account. The expectation-maximization (EM) algorithm is employed to infer the model parameters. As demonstrated with synthetic and experimental data, our approach outperforms the conventional MEF method, offering superior phase retrieval under challenging experimental conditions. This work underscores the significance of robust multi-exposure image fusion for ptychography, particularly in imaging shot-noise-dominated weakly scattering specimens or in cases where access to HDR detectors with high SNR is limited. Furthermore, the applicability of the Bayesian MEF approach extends beyond CDI to any imaging scheme that requires HDR treatment. Given this versatility, we provide the implementation of our algorithm as a Python package.

CurveMEF: Multi-exposure fusion via curve embedding network

Multi-exposure image fusion (MEF) aims to combine multiple images with different exposures into a single image to improve visual quality and preserve details. This paper proposes a curve embedding network for MEF (CurveMEF), which formulates the MEF task as estimating optimal fusion curves based on state feedback using pixel intensities as state variables. The fusion curve is embedded into CurveNet, a lightweight deep network, as a physical prior constraint. Leveraging the proposed physical informed MEF method, the fusion curve can adaptively adjust the pixel distribution of overexposed and underexposed regions based on the pixel intensities, and distinguish the importance of source images. CurveMEF supports both RGB and luminance channel inputs, demonstrating its flexibility. Experimental results show that CurveMEF achieves competitive performance compared to state-of-the-art methods in both qualitative and quantitative analysis. Moreover, CurveNet requires significantly fewer parameters and training data, enabling fast training and inference. The proposed method delivers high-speed and high-quality fusion while significantly reducing computational, providing an efficient and cost-effective solution. The code is publicly available at: https://github.com/PiratePai/CurveMEF.

Unsupervised Multi-Exposure Image Fusion Breaking Exposure Limits via Contrastive Learning

This paper proposes an unsupervised multi-exposure image fusion (MEF) method via contrastive learning, termed as MEF-CL. It breaks exposure limits and performance bottleneck faced by existing methods. MEF-CL firstly designs similarity constraints to preserve contents in source images. It eliminates the need for ground truth (actually not exist and created artificially) and thus avoids negative impacts of inappropriate ground truth on performance and generalization. Moreover, we explore a latent feature space and apply contrastive learning in this space to guide fused image to approximate normal-light samples and stay away from inappropriately exposed ones. In this way, characteristics of fused images (e.g., illumination, colors) can be further improved without being subject to source images. Therefore, MEF-CL is applicable to image pairs of any multiple exposures rather than a pair of under-exposed and over-exposed images mandated by existing methods. By alleviating dependence on source images, MEF-CL shows better generalization for various scenes. Consequently, our results exhibit appropriate illumination, detailed textures, and saturated colors. Qualitative, quantitative, and ablation experiments validate the superiority and generalization of MEF-CL. Our code is publicly available at https://github.com/hanna-xu/MEF-CL.

Multi-exposure image fusion via perception enhanced structural patch decomposition

Multi-exposure image fusion (MEF) is an affordable and convenient option for high-dynamic-range imaging. Current MEF methods are prone to visually unrealistic results since they take no account of perceptual factors. To address this problem, a multi-exposure image fusion method is proposed based on perception enhanced structural patch decomposition, namely PESPD-MEF. An image patch is first decomposed into four components: perceptual gain, signal strength, signal structure, and mean intensity. Then, the enhancement rule is designed for perceptual gain, and the latter three elements are fused independently in different ways. Finally, the fused components are aggregated to generate informative and perception-realistic results. Moreover, the multi-scale framework is adopted to boost the fused performance. The proposed method is also extended to address single low-light image enhancement issue. Experimental results demonstrate that the proposed approach outperforms the state-of-the-art methods by a large margin in terms of perceptual realism. The source code is available at https://github.com/Junchao2018/PESPD-MEF.

Multi-Exposure Image Fusion via Multi-Scale and Context-Aware Feature Learning

In this letter, a deep learning (DL)-based multi-exposure image fusion (MEF) method via multi-scale and context-aware feature learning is proposed, aiming to overcome the defects of existing traditional and DL-based methods. The proposed network is based on an auto-encoder architecture. First, an encoder that combines the convolutional network and Transformer is designed to extract multi-scale features and capture the global contextual information. Then, a multi-scale feature interaction (MSFI) module is devised to enrich the scale diversity of extracted features using cross-scale fusion and Atrous spatial pyramid pooling (ASPP). Finally, a decoder with a nest connection architecture is introduced to reconstruct the fused image. Experimental results show that the proposed method outperforms several representative traditional and DL-based MEF methods in terms of both visual quality and objective assessment. The source code and trained model are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yuliu316316/MSCA-MEF</uri> .

Smoke removal and image enhancement of laparoscopic images by an artificial multi-exposure image fusion method

In laparoscopic surgery, image quality is often degraded by surgical smoke or by side effects of the illumination system, such as reflections, specularities, and non-uniform illumination. The degraded images complicate the work of the surgeons and may lead to errors in image-guided surgery. Existing enhancement algorithms mainly focus on enhancing global image contrast, overlooking local contrast. Here, we propose a new Patch Adaptive Structure Decomposition utilizing the Multi-Exposure Fusion technique to enhance the local contrast of laparoscopic images for better visualization. The set of under-exposure level images is obtained from a single input blurred image by using gamma correction. Spatial linear saturation is applied to enhance image contrast and to adjust the image saturation. The Multi-Exposure Fusion (MEF) is used on a series of multi-exposure images to obtain a single clear and smoke-free fused image. MEF is applied by using adaptive structure decomposition on all image patches. Image entropy based on the texture energy is used to calculate image energy strength. The texture entropy energy determined the patch size that is useful in the decomposition of image structure. The proposed method effectively eliminate smoke and enhance the degraded laparoscopic images. The qualitative results showed that the visual quality of the resultant images is improved and smoke-free. Furthermore, the quantitative scores computed of the metrics: FADE, Blur, JNBM, and Edge Intensity are significantly improved as compared to other existing methods.

Tensor Product and Tensor-Singular Value Decomposition Based Multi-Exposure Fusion of Images

Considering multidimensional structure of the multi-exposure images, a new Tensor product and Tensor-singular value decomposition based Multi-Exposure image Fusion (TT-MEF) method is proposed. The main innovation of this work is to explore a new feature representation of multi-exposure images in the new tensor domain and design the fusion strategy on this basis. Specifically, the luminance and the chrominance channels are fused separately to maintain color consistency. For the luminance fusion, the luminance channel of multi-exposure images is divided into two parts, that is, de-mean term and mean term. The de-mean term is represented as a tensor to extract the feature. Then, the tensor product and tensor-singular value decomposition (T-SVD) are used to design a tensor feature extractor. Furthermore, a fusion strategy of the de-mean term is presented according to the visual saliency model, and a fusion strategy of the mean term is defined by the local and the global visual weights to control counterpoise between the local and global luminance. For the chrominance fusion, a new fusion strategy is also designed by the tensor product and T-SVD, similar to the luminance fusion. Finally, the fused image is obtained by combining the luminance and chrominance fusion. Experimental results show that the proposed TT-MEF method generally outperforms the existing state-of-the-art in terms of subjective visual quality and objective evaluation.

Multiexposure Image Fusion Method Based on Feature Weight of Image Sequence

Multiexposure Image Fusion Method Based on Feature Weight of Image Sequence

Multi-Scale Exposure Fusion Based on Multi-Visual Feature Measurement and Detail Enhancement Representation

Multi-exposure image fusion (MEF) is used to generate a high-quality image from a series of images with different exposure levels. The multi-scale-based MEF method achieves better fusion performance than single-scale-based method because it can better preserve the global contrast information. However, this method still has the problem of the loss of details in the fused results. To solve this problem, a novel multi-scale exposure image fusion method based on multi-visual feature measurement and detail enhancement representation in the IHS color space is proposed. First, three visual features of the source multi-exposure images, namely contrast, saturation, and exposure, are measured and are then adopted to construct the initial weight maps with adaptive weighting coefficients. Second, to optimize the initial weight maps and obtain the middle weight maps, a decision map construction method is proposed by comparing the pixel values of the detail maps of the intensity components, which can enhance the representation of detail information. Third, guided filtering is applied to eliminate the noise in the middle weight maps to obtain the final weight maps, which improves the visual effect of the fused image. Finally, image pyramid decomposition and reconstruction are performed on the source images and the final weight maps to achieve the final fused image. Numerical experimental results indicate that the proposed method outperforms state-of-the-art methods in terms of subjective visual and quantitative evaluations.

An Improved Multi-Exposure Image Fusion Method for Intelligent Transportation System

In this paper, an improved multi-exposure image fusion method for intelligent transportation systems (ITS) is proposed. Further, a new multi-exposure image dataset for traffic signs, TrafficSign, is presented to verify the method. In the intelligent transportation system, as a type of important road information, traffic signs are fused by this method to obtain a fused image with moderate brightness and intact information. By estimating the degree of retention of different features in the source image, the fusion results have adaptive characteristics similar to that of the source image. Considering the weather factor and environmental noise, the source image is preprocessed by bilateral filtering and dehazing algorithm. Further, this paper uses adaptive optimization to improve the quality of the output image of the fusion model. The qualitative and quantitative experiments on the new dataset show that the multi-exposure image fusion algorithm proposed in this paper is effective and practical in the ITS.

Hue-Correction Scheme Considering Non-Linear Camera Response for Multi-Exposure Image Fusion

We propose a novel hue-correction scheme for multi-exposure image fusion (MEF). Various MEF methods have so far been studied to generate higher-quality images. However, there are few MEF methods considering hue distortion unlike other fields of image processing, due to a lack of a reference image that has correct hue. In the proposed scheme, we generate an HDR image as a reference for hue correction, from input multi-exposure images. After that, hue distortion in images fused by an MEF method is removed by using hue information of the HDR one, on the basis of the constant-hue plane in the RGB color space. In simulations, the proposed scheme is demonstrated to be effective to correct hue-distortion caused by conventional MEF methods. Experimental results also show that the proposed scheme can generate high-quality images, regardless of exposure conditions of input multi-exposure images.

Multi-exposure image fusion based on recursive filter

Multi-exposure image fusion technology is to fuse images of the same scene with different exposures into a high-quality image that contains more scene information. This paper proposes a multi-exposure image fusion method based on a recursive filter. The method first calculates three characteristic indicators of the multi-exposure image sequence: image contrast, entropy, and exposure information. And use three characteristic indicators to estimate the initial image fusion. In order to obtain a more accurate weight map for image fusion, a recursive filter is used to refine the weight map. Finally, the weighted sum method is used to construct the fused image. The experimental results show that the fusion results obtained by the algorithm in this paper have high image clarity, better subjective visual fusion effects and objective performance indicators, and maintain a high computational efficiency.

GANFuse: a novel multi-exposure image fusion method based on generative adversarial networks

In this paper, a novel multi-exposure image fusion method based on generative adversarial networks (termed as GANFuse) is presented. Conventional multi-exposure image fusion methods improve their fusion performance by designing sophisticated activity-level measurement and fusion rules. However, these methods have a limited success in complex fusion tasks. Inspired by the recent FusionGAN which firstly utilizes generative adversarial networks (GAN) to fuse infrared and visible images and achieves promising performance, we improve its architecture and customize it in the task of extreme exposure image fusion. To be specific, in order to keep content of extreme exposure image pairs in the fused image, we increase the number of discriminators differentiating between fused image and extreme exposure image pairs. While, a generator network is trained to generate fused images. Through the adversarial relationship between generator and discriminators, the fused image will contain more information from extreme exposure image pairs. Thus, this relationship can realize better performance of fusion. In addition, the method we proposed is an end-to-end and unsupervised learning model, which can avoid designing hand-crafted features and does not require a number of ground truth images for training. We conduct qualitative and quantitative experiments on a public dataset, and the experimental result shows that the proposed model demonstrates better fusion ability than existing multi-exposure image fusion methods in both visual effect and evaluation metrics.

Multi-exposure image fusion based on tensor decomposition

In this paper, a multi-exposure image fusion (MEF) method is proposed based on tensor decomposition and saliency model. The main innovation of the proposed method is to explore a tensor domain for MEF and define the fusion rules based on tensor feature of higher order singular value decomposition (HOSVD) and saliency. Specifically, RGB images are converted to YCbCr images to maintain the stability of color information. For luminance channels, luminance patches of luminance images are constructed 3-order sub-tensors, and HOSVD is used to extract features of sub-tensors. Then, the sum of absolute coefficients (SAC) of weight coefficients are defined. Meanwhile, considering the impact of saliency on visual perception, visual saliency maps (VSMs) is used to evaluate luminance patches quality and guide the fusion rules to define the rule of fusion. For chrominance channels, VSMs of the chrominance channels is used to define fused rule. The experimental results show that the fused image with more texture details and saturated color is successfully generated by proposed method.

A Precise Multi-Exposure Image Fusion Method Based on Low-level Features.

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.

A Novel Patch-Based Multi-Exposure Image Fusion Using Super-Pixel Segmentation

A novel multi-exposure image fusion method is proposed for solving the problems of color distortion and detail loss through adaptive image patch segmentation. First, we use the super-pixel segmentation approach to divide the input images into the non-overlapping image patches composed of pixels with similar visual properties. Then, the image patches are decomposed into three independent components: signal strength, image structure and intensity. The three components are fused separately based on characteristics of human vision system and exposure level of input image. While, guided filtering is used to remove the blocking artifacts caused by patch-wise processing. In contrast to the existing methods which use fixed-size patches, the proposed method avoids blocking effect and preserves the color attribute of the input images. The experimental results show that the proposed method has advantages both in subjective and objective evaluation over the state-of-the-art multi-exposure fusion methods.

Multiexposure Estimation and Fusion Based on a Sparsity Exposure Dictionary

Multiexposure image fusion (MEF), which is an emerging hot research topic, aims to fuse multiexposure images into a high-quality low dynamic range (LDR) image. In this article, we propose a novel exposure estimation-based MEF method with sparse decomposition and a designed sparsity exposure dictionary (SED). First, in order to represent more elementary information of multiexposure images, SED is trained by underexposed and overexposed images and constructed by the K-means-based algorithm (K-SVD) algorithm. Next, the SED is applied to construct exposure estimation maps by the number of atoms of the image patch, which is obtained by sparse decomposition. When the exposure estimation maps are obtained, a novel fusion framework that uses these maps to fuse multiexposure images is presented. In this framework, we employ the exposure estimation maps and an adaptive guided filter to construct the final fusion decision maps. The two factors of guided filter are adaptively determined without manual interference to obtain an optimal representation effect for multiexposure image sequences with different sizes and number. Finally, a fused image is generated via the pyramid merging method. The proposed fusion method is conducted on a series of multiexposure image sequences and compared to seven popular MEF methods. The experimental results clearly demonstrate that the proposed method outperforms the state-of-the-art methods in terms of subjective visual and quantitative evaluations.

Deep Guided Learning for Fast Multi-Exposure Image Fusion.

We propose a fast multi-exposure image fusion (MEF) method, namely MEF-Net, for static image sequences of arbitrary spatial resolution and exposure number. We first feed a low-resolution version of the input sequence to a fully convolutional network for weight map prediction. We then jointly upsample the weight maps using a guided filter. The final image is computed by a weighted fusion. Unlike conventional MEF methods, MEF-Net is trained end-to-end by optimizing the perceptually calibrated MEF structural similarity (MEF-SSIM) index over a database of training sequences at full resolution. Across an independent set of test sequences, we find that the optimized MEF-Net achieves consistent improvement in visual quality for most sequences, and runs 10 to 1000 times faster than state-of-the-art methods. The code is made publicly available at.

Multi-exposure image fusion based on tensor decomposition and convolution sparse representation

In view of the problem about the loss of detail and color distortion in multi-exposure image fusion, this paper proposed a multi-exposure image fusion method based on tensor decomposition and convolution sparse representation. Tensor decomposition, as an approach of low-rank approximation for high-dimensional data, has great potential in feature extraction of multi-exposure images. Convolution sparse representation is a sparse optimization method for the whole image, which can preserve the detail information of the image to the greatest extent. At the same time, in order to avoid color distortion in the fused image, this paper adopted the method of separately fusing luminance and chrominance. Firstly, the core tensor of the source image was obtained by tensor decomposition. Besides, edge features were extracted on the first sub-band which contains the most information. Then the edge feature map was sparsely decomposed to obtain the activity level of each pixel by using L1 norm of the decomposition coefficient. Finally, take winner-take-all strategy to generate weight map so as to obtain the fused luminance components. Unlike the process of luminance fusion, chrominance components were fused by simple Gaussian weighting method, which solves the color distortion problem for the fused image to a certain extent. The experimental results show that the proposed method has great detail preservation ability.