Optimal HDR reconstruction with linear digital cameras

Granados, Miguel; Ajdin, Boris; Wand, Michael; Theobalt, Christian; Seidel, Hans‐Peter; Lensch, Hendrik P. A.

doi:10.1109/cvpr.2010.5540208

Cited by 147 publications

(174 citation statements)

References 16 publications

Supporting

Mentioning

170

Contrasting

Order By: Relevance

“…An overview of all these different methods for the generation of HDR image from multi-exposure images of a static scene can be found in [22]. Assuming CRF is known, Granados et al recently developed a method to generate an HDR image in the presence of various types of noise [23]. The generated HDR image can then be encoded in Radiance RGBE (.hdr) or Open EXR (.exr) formats which employ floating point numbers to store the intensity values.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An Improved HDR Technique by Classification Method

Agarwal¹,

Shrivastava²,

Bisen³

2015

IJCA

View full text Add to dashboard Cite

Now a day, HDR image is mostly used in applications of wide range such as next generation broadcast, digital cinema and digital photography, because of its high quality and its good expression ability. In our work we have improved an HDR (High Dimensional image) and changed it into multiple improved Low Dynamic Range (LDR) images. We further used multiple copies of the same image and classified differently as per new Low Dynamic Range. Hence we obtain many new exponentially improved images. These images can be further improved by further LDR technique. Finally, we combine all these images and obtain a final image. This final image will be better as compared to the original image. Hence we have an image which in itself will be improved version of the given image.

show abstract

Section: Related Workmentioning

confidence: 99%

“…An example of tone mapping is the gradient domain HDR compression method by Fattal et al [23]. An overview of the different types of tone mapping operators (global and local) can be found in [22].…”

Section: Related Workmentioning

confidence: 99%

An Improved HDR Technique by Classification Method

Agarwal¹,

Shrivastava²,

Bisen³

2015

IJCA

View full text Add to dashboard Cite

show abstract

“…Much research has focused on the combination of the LDR images, assuming perfect registration and completely static scenes. The pioneering work of Mann and Picard [7] and Devebec and Malik [8] has been refined and extended by recent work aiming at defining optimal weights based on the noise characteristics of the sensor (e.g., Akyüz and Reinhard [9], Hasinoff et al [10], Granados et al [11], Robertson et al [12]). When the same pixel across the stack captures irradiance from different objects in the scene, whether because of camera motion or changes in the scene, it generates artifacts commonly referred to as ghosting because the averaging process produces transparent copies of the moving objects.…”

Section: Related Workmentioning

confidence: 99%

Exposure Stacks of Live Scenes with Hand-Held Cameras

Gallo

Pulli

2012

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Many computational photography applications require the user to take multiple pictures of the same scene with different camera settings. While this allows to capture more information about the scene than what is possible with a single image, the approach is limited by the requirement that the images be perfectly registered. In a typical scenario the camera is hand-held and is therefore prone to moving during the capture of an image burst, while the scene is likely to contain moving objects. Combining such images without careful registration introduces annoying artifacts in the final image. This paper presents a method to register exposure stacks in the presence of both camera motion and scene changes. Our approach warps and modifies the content of the images in the stack to match that of a reference image. Even in the presence of large, highly non-rigid displacements we show that the images are correctly registered to the reference.

show abstract

“…More sophisticated methods, for example using per-pixel non-linear optimization, are difficult to apply directly, particularly in a realtime context [5][6] [7]. In this work we decompose the problem in a novel way, enabling non-linear optimization for realtime HDR video.…”

Section: Motivationmentioning

confidence: 99%

“…In this section, we review this approach, which is also the most popular framework in prior work [7] [8]. Estimatesq i (x) ∈ R ≥0 of the photoquantity q at…”

Section: Prior Workmentioning

confidence: 99%

Comparametric image compositing: Computationally efficient high dynamic range imaging

Ali

Mann

2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

We propose a novel computational method for compositing low-dynamic-range (LDR) images into an high-dynamicrange (HDR) image by the use of a comparametric camera response function (CCRF), which is the response of a virtual HDR camera to multiple inputs.We demonstrate the use of this method with a simple probabilistic joint estimation model, that accounts for Gaussian noise, using iterative non-linear optimization to compute the CCRF. We achieve a speedup of ≈2500×, relative to direct calculation using the probabilistic model. This method can be implemented as a multidimensional lookup table, and enables realtime HDR video with any camera response function model, and any compositing algorithm based on pixel value and exposure.

show abstract

Optimal HDR reconstruction with linear digital cameras

Abstract: Abstract

Cited by 147 publications

References 16 publications

An Improved HDR Technique by Classification Method

An Improved HDR Technique by Classification Method

Exposure Stacks of Live Scenes with Hand-Held Cameras

Comparametric image compositing: Computationally efficient high dynamic range imaging

Contact Info

Product

Resources

About