HDR imaging under non-uniform blurring
07 Oct 2012-pp 451-460
TL;DR: A technique to obtain the high dynamic range (HDR) irradiance of a scene from a set of differently exposed images captured using a hand-held camera and a transformation spread function (TSF) that represents space-variant blurring as a weighted average of differently transformed versions of the latent image.
Abstract: Knowledge of scene irradiance is necessary in many computer vision algorithms. In this paper, we develop a technique to obtain the high dynamic range (HDR) irradiance of a scene from a set of differently exposed images captured using a hand-held camera. Any incidental motion induced by camera-shake can result in non-uniform motion blur. This is particularly true for frames captured with high exposure durations. We model the motion blur using a transformation spread function (TSF) that represents space-variant blurring as a weighted average of differently transformed versions of the latent image. We initially estimate the TSF of the blurred frames and then estimate the latent irradiance of the scene.
Citations
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TL;DR: This work proposes a passive method to automatically detect image splicing using blur as a cue and can expose the presence of splicing by evaluating inconsistencies in motion blur even under space-variant blurring situations.
Abstract: The extensive availability of sophisticated image editing tools has rendered it relatively easy to produce fake images. Image splicing is a form of tampering in which an original image is altered by copying a portion from a different source. Because the phenomenon of motion blur is a common occurrence in hand-held cameras, we propose a passive method to automatically detect image splicing using blur as a cue. Specifically, we address the scenario of a static scene in which the cause of blur is due to hand shake. Existing methods for dealing with this problem work only in the presence of uniform space-invariant blur. In contrast, our method can expose the presence of splicing by evaluating inconsistencies in motion blur even under space-variant blurring situations. We validate our method on several examples for different scene situations and camera motions of interest.
36Â citations
Additional excerpts
...We estimate the weights of these homographies also referred to as Transformation Spread Function (TSF) [22], [32] from a single blurred image....
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TL;DR: A method is developed that takes input non-uniformly blurred and differently exposed images to extract the deblurred, latent irradiance image and estimates the TSFs of the blurred images from locally derived point spread functions by exploiting their linear relationship.
Abstract: Hand-held cameras inevitably result in blurred images caused by camera-shake, and even more so in high dynamic range imaging applications where multiple images are captured over a wide range of exposure settings. The degree of blurring depends on many factors such as exposure time, stability of the platform, and user experience. Camera shake involves not only translations but also rotations resulting in nonuniform blurring. In this paper, we develop a method that takes input non-uniformly blurred and differently exposed images to extract the deblurred, latent irradiance image. We use transformation spread function (TSF) to effectively model the blur caused by camera motion. We first estimate the TSFs of the blurred images from locally derived point spread functions by exploiting their linear relationship. The scene irradiance is then estimated by minimizing a suitably derived cost functional. Two important cases are investigated wherein 1) only the higher exposures are blurred and 2) all the captured frames are blurred.
19Â citations
Cites background from "HDR imaging under non-uniform blurr..."
...In a preliminary version of this work [32], we had considered the input data to consist of both non-blurred and blurred observations, assuming that the effect of camera-shake is significant only beyond a certain exposure duration....
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11 Jul 2016
TL;DR: This work presents a robust HDR imaging system which can deal with blurry LDR images, overcoming the limitations of most existing HDR methods.
Abstract: High dynamic range (HDR) images can show more details and luminance information in general display device than low dynamic image (LDR) images. We present a robust HDR imaging system which can deal with blurry LDR images, overcoming the limitations of most existing HDR methods. Experiments on real images show the effectiveness and competitiveness of the proposed method.
1Â citations
Cites background from "HDR imaging under non-uniform blurr..."
...[6] discussed a complex approach to obtain the HDR image from a set of frames where images captured with high exposures were non-uniformly blurred due to camera shake....
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30 Oct 2014-International Journal of Advanced Research in Electrical, Electronics and Instrumentation Energy
TL;DR: This paper proposes a method for image deblurring and reconstruction of HDR images using transformation spread functions (TSFs), which is directly estimated from locally derived point spread function (PSFs) by exploiting their relationship.
Abstract: Image blur is difficult to avoid in many situations and can often ruin a photograph. Image deblurring and restoration is necessary in digital image processing. Image deblurring is a process, which is used to make pictures sharp and useful by using mathematical model. Image deblurring have wide applications from consumer photography, e.g., remove motion blur due to camera shake, to radar imaging and tomography, e.g., remove the effect of imaging system response. In this paper we propose a method for image deblurring and reconstruction of HDR images using transformation spread functions (TSFs), which is directly estimated from locally derived point spread functions (PSFs) by exploiting their relationship. We are also calculating the quality Measurement parameters of images.
1Â citations
TL;DR: This work focuses on the tone mapping techniques with a practice to dynamically determine the suitable exposure parameter for LDR images agreeing to the property of each scene to be seized, which provides approximately 10% improvement in UIQI in comparison to C.Vijay method.
Abstract: It is the age of fast and good quality Digital images, those are subject to blurring due to many hardware limitations, such as atmospheric trouble, apparatus noise and poor focus quality. A high quality image restoration is done by making a High dynamic range (HDR). The (HDR) image generation had been studied in past years. Due to the expense and lack of HDR cameras, a lot of works try to generate HDR images using several low dynamic range (LDR) images with different exposure setting. To ensure high-class HDR image generation and details of the scene should be retained in different LDR images and the exposure parameters of LDR image should be chosen carefully. In this paper our proposed work focuses on the tone mapping techniques with a practice to dynamically determine the suitable exposure parameter for LDR images agreeing to the property of each scene to be seized. The proposed method provides approximately 10% improvement in UIQI in comparison to C.S.Vijay method. Simulation results reveal that better HDR images are always generated with the LDR images held by the determined exposure, compared to those produced by the method with fixed exposures.
1Â citations
References
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TL;DR: In this article, the tone reproduction problem is also considered, which maps the potentially high dynamic range of real world luminances to the low dynamic ranges of the photographic print, which is a classic photographic task.
Abstract: A classic photographic task is the mapping of the potentially high dynamic range of real world luminances to the low dynamic range of the photographic print. This tone reproduction problem is also ...
637Â citations
TL;DR: A new method for rendering high dynamic range images on conventional displays that is conceptually simple, computationally efficient, robust, and easy to use is presented.
Abstract: We present a new method for rendering high dynamic range images on conventional displays. Our method is conceptually simple, computationally efficient, robust, and easy to use. We manipulate the gr...
557Â citations
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TL;DR: This work proposes a technique for fusing a bracketed exposure sequence into a high quality image, without converting to HDR first, which avoids camera response curve calibration and is computationally efficient.
Abstract: We propose a technique for fusing a bracketed exposure sequence into a high quality image, without converting to HDR first. Skipping the physically-based HDR assembly step simplifies the acquisition pipeline. This avoids camera response curve calibration and is computationally efficient. It also allows for including flash images in the sequence. Our technique blends multiple exposures, guided by simple quality measures like saturation and contrast. This is done in a multiresolution fashion to account for the brightness variation in the sequence. The resulting image quality is comparable to existing tone mapping operators.
447Â citations
Book•
21 Nov 2005
TL;DR: This landmark book is the first to describe HDRI technology in its entirety and covers a wide-range of topics, from capture devices to tone reproduction and image-based lighting, leading to an unparalleled visual experience.
Abstract: This landmark book is the first to describe HDRI technology in its entirety and covers a wide-range of topics, from capture devices to tone reproduction and image-based lighting. The techniques described enable you to produce images that have a dynamic range much closer to that found in the real world, leading to an unparalleled visual experience. As both an introduction to the field and an authoritative technical reference, it is essential to anyone working with images, whether in computer graphics, film, video, photography, or lighting design.
New material includes chapters on High Dynamic Range Video Encoding, High Dynamic Range Image Encoding, and High Dynammic Range Display Devices
Written by the inventors and initial implementors of High Dynamic Range Imaging
Covers the basic concepts (including just enough about human vision to explain why HDR images are necessary), image capture, image encoding, file formats, display techniques, tone mapping for lower dynamic range display, and the use of HDR images and calculations in 3D rendering
Range and depth of coverage is good for the knowledgeable researcher as well as those who are just starting to learn about High Dynamic Range imaging
Table of Contents
Introduction; Light and Color; HDR Image Encodings; HDR Video Encodings; HDR Image and Video Capture; Display Devices; The Human Visual System and HDR Tone Mapping; Spatial Tone Reproduction; Frequency Domain and Gradient Domain Tone Reproduction; Inverse Tone Reproduction; Visible Difference Predictors; Image-Based Lighting.
417Â citations
05 Sep 2010
TL;DR: A novel single image deblurring method to estimate spatially non-uniform blur that results from camera shake that out-performs current approaches which make the assumption of spatially invariant blur.
Abstract: We present a novel single image deblurring method to estimate spatially non-uniform blur that results from camera shake. We use existing spatially invariant deconvolution methods in a local and robust way to compute initial estimates of the latent image. The camera motion is represented as a Motion Density Function (MDF) which records the fraction of time spent in each discretized portion of the space of all possible camera poses. Spatially varying blur kernels are derived directly from the MDF. We show that 6D camera motion is well approximated by 3 degrees of motion (in-plane translation and rotation) and analyze the scope of this approximation. We present results on both synthetic and captured data. Our system out-performs current approaches which make the assumption of spatially invariant blur.
388Â citations