Multi-Exposure Document Fusion Based on Edge-Intensities

Block, Marco; Maxim, Schaubert,; Wiesel, Fabian; Rojas, Raúl

doi:10.1109/icdar.2009.142

Cited by 3 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method usually follows the following processing chain: In the context of document image exposure bracketing, the literature we found is really limited. The only paper that discloses document image exposure bracketing techniques can be found in paper [4], where an adaptive image fusion was used. However, there are two limitations in this paper: (1) the author assumes that document images are captured with a fixed camera and hence there is no need to perform image registration.…”

Section: Technical Overview Of Exposure Bracketing Techniquesmentioning

confidence: 99%

Exposure Bracketing Techniques for Camera Document Image Enhancement

Liu

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

Capturing document images using digital cameras in uneven lighting conditions is challenging, leading to poorly captured images, which hinders the processing that follows, such as Optical Character Recognition (OCR). In this paper, we propose the use of exposure bracketing techniques to solve this problem. Instead of capturing one image, we used several images that were captured with different exposure settings and used the exposure bracketing technique to generate a high-quality image that incorporates useful information from each image. We found that this technique can enhance image quality and provides an effective way of improving OCR accuracy. Our contributions in this paper are two-fold: (1) a preprocessing chain that uses exposure bracketing techniques for document images is discussed, and an automatic registration method is proposed to find the geometric disparity between multiple document images, which lays the foundation for exposure bracketing; (2) several representative exposure bracketing algorithms are incorporated in the processing chain and their performances are evaluated and compared.

show abstract

Section: Technical Overview Of Exposure Bracketing Techniquesmentioning

confidence: 99%

Exposure Bracketing Techniques for Camera Document Image Enhancement

Liu

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The quality of the fused image is measured by the local edge intensity L in image I [38]. It folds a Gaussian kernel G with the image I to get a smoothed image.…”

Section: Edge Intensitymentioning

confidence: 99%

A Novel Multi-Focus Image Fusion Method Based on Stochastic Coordinate Coding and Local Density Peaks Clustering

Zhu

Chai

et al. 2016

Future Internet

View full text Add to dashboard Cite

The multi-focus image fusion method is used in image processing to generate all-focus images that have large depth of field (DOF) based on original multi-focus images. Different approaches have been used in the spatial and transform domain to fuse multi-focus images. As one of the most popular image processing methods, dictionary-learning-based spare representation achieves great performance in multi-focus image fusion. Most of the existing dictionary-learning-based multi-focus image fusion methods directly use the whole source images for dictionary learning. However, it incurs a high error rate and high computation cost in dictionary learning process by using the whole source images. This paper proposes a novel stochastic coordinate coding-based image fusion framework integrated with local density peaks. The proposed multi-focus image fusion method consists of three steps. First, source images are split into small image patches, then the split image patches are classified into a few groups by local density peaks clustering. Next, the grouped image patches are used for sub-dictionary learning by stochastic coordinate coding. The trained sub-dictionaries are combined into a dictionary for sparse representation. Finally, the simultaneous orthogonal matching pursuit (SOMP) algorithm is used to carry out sparse representation. After the three steps, the obtained sparse coefficients are fused following the max L1-norm rule. The fused coefficients are inversely transformed to an image by using the learned dictionary. The results and analyses of comparison experiments demonstrate that fused images of the proposed method have higher qualities than existing state-of-the-art methods.

show abstract

“…Many methods have been proposed to obtain a more informative image using multiple exposure techniques. Of these methods, the multi-scale decomposition (MSD) approach appears to be a particularly efficient and effective image fusion scheme [21][22][23]. In this scheme, the source images are transformed with MSD separately and merged at different scales when guided by a selected feature measurement.…”

Section: Image Fusion and Shadow Suppressionmentioning

confidence: 99%

“…The composite image is then reconstructed by an inverse transform. Commonly used MSD methods for image fusion are Laplacian [24], contrast [25], gradient pyramid transforms [22,26] and wavelet transform [27,28]. The entropy, gradient, saturation and/or edge intensity are the features used to determine the weight map at different scales [21][22][23].…”

Section: Image Fusion and Shadow Suppressionmentioning

confidence: 99%

“…Commonly used MSD methods for image fusion are Laplacian [24], contrast [25], gradient pyramid transforms [22,26] and wavelet transform [27,28]. The entropy, gradient, saturation and/or edge intensity are the features used to determine the weight map at different scales [21][22][23]. In the proposed system, we apply the contrast pyramid transform to merge the overexposed image and the underexposed image, and to create a new image in which all areas are well exposed and shadows are suppressed.…”

Section: Image Fusion and Shadow Suppressionmentioning

confidence: 99%

See 1 more Smart Citation

Fusing complementary images for pavement cracking measurements

Yao

Zhao

Yao

et al. 2015

Meas. Sci. Technol.

View full text Add to dashboard Cite

Cracking is a major pavement distress that jeopardizes road serviceability and traffic safety. Automated pavement distress survey (APDS) systems have been developed using digital imaging technology to replace human surveys for more timely and accurate inspections. Most APDS systems require special lighting devices to illuminate pavements and prevent shadows of roadside objects that distort cracks in the image. Most artificial lighting devices are laser based, and are either hazardous to unprotected people or require dedicated power supplies on the vehicle. This study was aimed to develop a new imaging system that can scan pavement surface at highway speed and determine the level of severity of pavement cracking without using any artificial lighting. The new system consists of dual line-scan cameras that are installed side by side to scan the same pavement area as the vehicle moves. Cameras are controlled with different exposure settings so that both sunlit and shadowed areas can be visible in two separate images. The paired images contain complementary details useful for reconstructing an image in which the shadows are eliminated. This paper intends to present (1) the design of the dual line-scan camera system, (2) a new calibration method for line-scan cameras to rectify and register paired images, (3) a customized image-fusion algorithm that merges the multi-exposure images into one shadow-free image for crack detection, and (4) the results of the field tests on a selected road over a long period.

show abstract

Multi-Exposure Document Fusion Based on Edge-Intensities

Cited by 3 publications

References 12 publications

Exposure Bracketing Techniques for Camera Document Image Enhancement

Exposure Bracketing Techniques for Camera Document Image Enhancement

A Novel Multi-Focus Image Fusion Method Based on Stochastic Coordinate Coding and Local Density Peaks Clustering

Fusing complementary images for pavement cracking measurements

Contact Info

Product

Resources

About