HDR imaging under non-uniform blurring

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.

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.

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.

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.

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.

Abstract: We present a method of recovering high dynamic range radiance maps from photographs taken with conventional imaging equipment. In our method, multiple photographs of the scene are taken with different amounts of exposure. Our algorithm uses these differently exposed photographs to recover the response function of the imaging process, up to factor of scale, using the assumption of reciprocity. With the known response function, the algorithm can fuse the multiple photographs into a single, high dynamic range radiance map whose pixel values are proportional to the true radiance values in the scene. We demonstrate our method on images acquired with both photochemical and digital imaging processes. We discuss how this work is applicable in many areas of computer graphics involving digitized photographs, including image-based modeling, image compositing, and image processing. Lastly, we demonstrate a few applications of having high dynamic range radiance maps, such as synthesizing realistic motion blur and simulating the response of the human visual system.

Abstract: Camera shake during exposure leads to objectionable image blur and ruins many photographs. Conventional blind deconvolution methods typically assume frequency-domain constraints on images, or overly simplified parametric forms for the motion path during camera shake. Real camera motions can follow convoluted paths, and a spatial domain prior can better maintain visually salient image characteristics. We introduce a method to remove the effects of camera shake from seriously blurred images. The method assumes a uniform camera blur over the image and negligible in-plane camera rotation. In order to estimate the blur from the camera shake, the user must specify an image region without saturation effects. We show results for a variety of digital photographs taken from personal photo collections.

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 faced by computer graphics practitioners who map digital images to a low dynamic range print or screen. The work presented in this paper leverages the time-tested techniques of photographic practice to develop a new tone reproduction operator. In particular, we use and extend the techniques developed by Ansel Adams to deal with digital images. The resulting algorithm is simple and produces good results for a wide variety of images.

Abstract: Foreword Preface 1 Introduction 2 Light And Color 3 HDR Image Encodings 4 HDR Image Capture 5 Display Devices 6 The Human Visual System and HDR Tone Mapping 7 Spatial Tone Reproduction 8 Frequency Domain and Gradient Domain Tone Reproduction 9 Image-Based Lighting List of Symbols References Index

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 gradient field of the luminance image by attenuating the magnitudes of large gradients. A new, low dynamic range image is then obtained by solving a Poisson equation on the modified gradient field. Our results demonstrate that the method is capable of drastic dynamic range compression, while preserving fine details and avoiding common artifacts, such as halos, gradient reversals, or loss of local contrast. The method is also able to significantly enhance ordinary images by bringing out detail in dark regions.