Fast exposure fusion using exposedness function

Nejati, Mansour; Karimi, Maryam; Soroushmehr, S. M. Reza; Karimi, Nader; Samavi, Shadrokh; Najarian, Kayvan

doi:10.1109/icip.2017.8296679

Cited by 43 publications

(52 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(a) Mertens [5] (b) Sakai [9] (c) Nejati [10] (d) Proposed with [5] (e) Proposed with [9] (f) Proposed with [10] Fig. 4: Images I f fused by six methods Figure 4 shows images I f produced by each multi-exposure image fusion method.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Mertens [5] Sakai [9] Nejati [10] image Original Proposed Original Proposed Original Proposed Corridor 0.0000 0.0000 0.3750 0.0000 0.3736 0.0000 0.3637 Lobby 0.0006 0.0063 0.5988 0.0058 0.6035 0.0054 0.5541 Window 0.0044 0.0333 0.4679 0.0308 0.4644 0.0261 0.5080 Table 5: Experimental results for Simulation 2 (discrete entropy). Boldface indicates the higher score.…”

Section: Methods Inputmentioning

confidence: 99%

“…Enhanced multi-exposure images I i can be used as input for any existing multi-exposure image fusion methods. While numerous methods for fusing images have been proposed, here we use those of Mertens et al [5], Sakai et al [9], and Nejati et al [10]. A final image I f is produced as…”

Section: Fusion Of Enhanced Multi-exposure Imagesmentioning

confidence: 99%

“…Because of such a situation, the interest of multi-exposure image fusion has recently been increasing. Various research works on multi-exposure image fusion have so far been reported [4][5][6][7][8][9][10]. These fusion methods utilize a set of differently exposed images, "multi-exposure images", and fuse them to produce an image with high quality.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Multi-Exposure Image Fusion Based on Exposure Compensation

Kinoshita

Shiota

Kiya

et al. 2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

This paper proposes a novel multi-exposure image fusion method based on exposure compensation. Multi-exposure image fusion is a method to produce images without color saturation regions, by using photos with different exposures. However, in conventional works, it is unclear how to determine appropriate exposure values, and moreover, it is difficult to set appropriate exposure values at the time of photographing due to time constraints. In the proposed method, the luminance of the input multi-exposure images is adjusted on the basis of the relationship between exposure values and pixel values, where the relationship is obtained by assuming that a digital camera has a linear response function. The use of a local contrast enhancement method is also considered to improve input multi-exposure images. The compensated images are finally combined by one of existing multi-exposure image fusion methods. In some experiments, the effectiveness of the proposed method are evaluated in terms of the tone mapped image quality index, statistical naturalness, and discrete entropy, by comparing the proposed one with conventional ones.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Section: Methods Inputmentioning

confidence: 99%

Section: Fusion Of Enhanced Multi-exposure Imagesmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Multi-Exposure Image Fusion Based on Exposure Compensation

Kinoshita

Shiota

Kiya

et al. 2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

show abstract

“…Because of such a situation, the interest of multi-exposure image fusion has recently been increasing. Various research works on multi-exposure image fusion have so far been reported [4][5][6][7][8][9][10][11]. These fusion methods utilize a set of differently exposed images, "multi-exposure images", and fuse them to produce an image with high quality.…”

Section: Introductionmentioning

confidence: 99%

Automatic Exposure Compensation for Multi-Exposure Image Fusion

Kinoshita

Shiota

Kiya

2018

2018 25th IEEE International Conference on Image Processing (ICIP)

View full text Add to dashboard Cite

This paper proposes a novel luminance adjustment method based on automatic exposure compensation for multi-exposure image fusion. Multi-exposure image fusion is a method to produce images without saturation regions, by using photos with different exposures. In conventional works, it has been pointed out that the quality of those multi-exposure images can be improved by adjusting the luminance of them. However, how to determine the degree of adjustment has never been discussed. This paper therefore proposes a way to automatically determines the degree on the basis of the luminance distribution of input multi-exposure images. Moreover, new weights, called "simple weights", for image fusion are also considered for the proposed luminance adjustment method. Experimental results show that the multi-exposure images adjusted by the proposed method have better quality than the input multi-exposure ones in terms of the well-exposedness. It is also confirmed that the proposed simple weights provide the highest score of statistical naturalness and discrete entropy in all fusion methods.

show abstract

A novel multimodality anatomical image fusion method based on contrast and structure extraction

Sufyan

Imran

Shah

et al. 2021

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Image modalities, such as computed tomography (CT), magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT), and so on, reflect various levels of details about objects of interest that help medical practitioners to examine patients' diseases from different perspectives. A single medical image, at times, may not be sufficient for making a critical decision; therefore, providing detailed information from a different perspective may help in making a better decision. Image fusion techniques play a vital role in this regard by combining important details from different medical images into a single, information enhanced image. In this article, we present a novel weighted term multimodality anatomical medical image fusion method. The proposed method, as a first step, eliminates the distortions from the source images and afterward, extracts two pieces of crucial information: the local contrast and the salient structure. Both the local contrast and salient structure are later combined to obtain the final weight map. The obtained weights are then passed through a fast guided filter to remove the discontinuities and noise.Lastly, the refined weight map is fused with source images using pyramid decomposition to get the final fused image. The proposed method is accessed and compared both qualitatively and quantitatively with state-of-the-art techniques. The result illustrates the performance superiority and efficiency of the proposed method.

show abstract

Fast exposure fusion using exposedness function

Cited by 43 publications

References 17 publications

Multi-Exposure Image Fusion Based on Exposure Compensation

Multi-Exposure Image Fusion Based on Exposure Compensation

Automatic Exposure Compensation for Multi-Exposure Image Fusion

A novel multimodality anatomical image fusion method based on contrast and structure extraction

Contact Info

Product

Resources

About