Blurred Versions Research Articles

Image and depth from a conventional camera with a coded aperture

A conventional camera captures blurred versions of scene information away from the plane of focus. Camera systems have been proposed that allow for recording all-focus images, or for extracting depth, but to record both simultaneously has required more extensive hardware and reduced spatial resolution. We propose a simple modification to a conventional camera that allows for the simultaneous recovery of both (a) high resolution image information and (b) depth information adequate for semi-automatic extraction of a layered depth representation of the image. Our modification is to insert a patterned occluder within the aperture of the camera lens, creating a coded aperture. We introduce a criterion for depth discriminability which we use to design the preferred aperture pattern. Using a statistical model of images, we can recover both depth information and an all-focus image from single photographs taken with the modified camera. A layered depth map is then extracted, requiring user-drawn strokes to clarify layer assignments in some cases. The resulting sharp image and layered depth map can be combined for various photographic applications, including automatic scene segmentation, post-exposure refocusing, or re-rendering of the scene from an alternate viewpoint.

An efficient algorithm for superresolution in medium field imaging

In this paper, we study the problem of reconstruction of a high-resolution (HR) image from several blurred low-resolution (LR) image frames in medium field. The image frames consist of blurred, decimated, and noisy versions of a HR image. The HR image is modeled as a Markov random field (MRF), and a maximum a posteriori (MAP) estimation technique is used for the restoration. We show that with the periodic boundary condition, a HR image can be restored efficiently by using fast Fourier transforms. We also apply the preconditioned conjugate gradient method to restore HR images in the aperiodic boundary condition. Computer simulations are given to illustrate the effectiveness of the proposed approach.

Hierarchical Star Formation in the Spiral Galaxy NGC 628

The distributions of size and luminosity for star-forming regions in the nearby spiral galaxy NGC 628 are studied over a wide range of scales using progressively blurred versions of an image from the Hubble Space Telescope Advanced Camera for Surveys. Four optical filters are considered for the central region, including H?. Two filters are used for an outer region. The features in each blurred image are counted and measured using SExtractor. The cumulative size distribution is found to be a power law in all passbands, with a slope of approximately -1.5 over 1.8 orders of magnitude. The luminosity distribution is approximately a power law as well, with a slope of approximately -1 for logarithmic intervals of luminosity. The results suggest a scale-free nature for stellar aggregates in a galaxy disk. Fractal models of thin disks reproduce the projected size distribution and suggest a projected mass distribution slope of approximately -0.5 for these extended regions. This mass slope converts to the observed luminosity slope if we account for luminosity evolution and longer lifetimes in larger regions.

A Fast Algorithm for Image Super-Resolution from Blurred Observations

We study the problem of reconstruction of a high-resolution image from several blurred low-resolution image frames. The image frames consist of blurred, decimated, and noisy versions of a high-resolution image. The high-resolution image is modeled as a Markov random field (MRF), and a maximum a posteriori (MAP) estimation technique is used for the restoration. We show that with the periodic boundary condition, a high-resolution image can be restored efficiently by using fast Fourier transforms. We also apply the preconditioned conjugate gradient method to restore high-resolution images in the aperiodic boundary condition. Computer simulations are given to illustrate the effectiveness of the proposed approach.

Bone tumor segmentation from MR perfusion images with neural networks using multi-scale pharmacokinetic features

Abstract Bone tumor segmentation and the distinction between viable and non-viable tumor tissue is required during the follow-up of chemotherapeutical treatment. Monitoring viable tumor area over time is important in the ongoing assessment of the effect of preoperative chemotherapy. In this paper, features derived from a pharmacokinetic model of tissue perfusion are investigated. A multi-scale analysis of the parametric perfusion images is applied to incorporate contextual information. A feed-forward neural network is proposed to classify pixels into viable, non-viable tumor, and healthy tissue. We elaborate on the design of a cascaded classifier and analyze the contribution of the different features to its performance. Multi-scale blurred versions of the parametric images together with a multi-scale formulation of the local image entropy turned out to be the most relevant features in distinguishing the tissues of interest. We experimented with an architecture consisting of cascaded neural networks to cope with uneven class distributions. The classification of each pixel was obtained by weighting the results of five bagged neural networks with either the mean or median rules. The experiments indicate that both the mean and median rules perform equally well.

Cognitive issues in image quality measurement

Designers of imaging systems, image processing algorithms, etc., usually take for granted that methods for assessing perceived image quality produce unbiased estimates of the viewers’ quality impression. Quality judgments, however, are affected by the judgment strategies induced by the experimental procedures. In this paper the results of two experiments are presented illustrating the influence judgment strategies can have on quality judgments. The first experiment concerns contextual effects due to the composition of the stimulus set. Subjects assessed the sharpness of two differently composed sets of blurred versions of one static image. The sharpness judgments for the blurred images present in both stimulus sets were found to be dependent on the composition of the set as well as the scaling technique employed. In the second experiment subjects assessed either the overall quality or the overall impairment of manipulated and standard JPEG-coded images containing two main artifacts. The results indicate a systematic difference between the quality and impairment judgments that could be interpreted as instruction-based different weighting of the two artifacts. Again, some influence of scaling technique was observed. The results of both experiments underscore the important role judgment strategies play in the psychophysical evaluation of image quality. Ignoring this influence on quality judgments may lead to invalid conclusions about the viewers’ impression of image quality.

Generalized transfer function estimation using evolutionary spectral deblurring

We present a method for estimating the generalized transfer function (GTF) of a time-varying filter from a time-frequency representation (TFR) of its output. This method uses the fact that many TFR's can be written as blurred versions of the GTF. The approach minimizes the error between the TFR found from the data and that found by blurring the GTF. The problem as such has many solutions. We, therefore, additionally constrain it to minimize the distance between the GTF-based spectrum and the autoterms of the Wigner distribution, suppressing the cross terms using an appropriate signal dependent mask function. To illustrate the performance of the proposed procedure, we apply it to the spectral representation of speech signals and to signal masking.

Your eyes only? A test of interactive influence in the processing of facial features.

The holistic hypothesis in face processing was tested in 3 experiments. Holistic processing was conceptualized as interactive influence of facial features on the perceptual representation of faces. In Experiment 1, 3 facial features (eye distance, width of nose, size of mouth) were varied on 3 values per feature. Photographs and blurred versions were used. Participants assigned each stimulus face to 1 of 2 target faces according to similarity. The data were evaluated by the logit model that provides a direct test of interactive influence of the features on participants' performance. The interactive-processing hypothesis was not confirmed. The results were replicated in Experiment 2, in which 2 features with 5 values each were used and data of individual participants were evaluated, and in Experiment 3, in which a reduced presentation time of 250 ms was used. It is concluded that facial features are processed and represented independently.

Exact multichannel blind image restoration

Liu and Xu (see ibid., vol.43, no.11, p.2714-23, 1995) proposed a blind symbol estimation algorithm in digital communications. If all the channels are linear time-invariant finite impulse response (LTI FIR) filters, this algorithm can estimate symbols exactly when the channels and the input symbols satisfy some loose restrictions. We extend this algorithm to two dimensions, and in particular, to multichannel (noise-free) blind image restoration. Assuming linear FIR blur functions, given at least three blurred versions of the same image, and loose restrictions (different from those of Liu and Xu) on the original image and on the blur functions, the original image can be exactly recovered, up to a scalar multiplier.

Multidimensional system identification through blind deconvolution

By invoking characteristics of the recently introduced zero-sheet of the spectrum of a signal having finite (or compact) support, it is noted that the multidimensional system identification problem should be solvable through blind deconvolution, that is, the system response function should be inferrable in the absence of prior knowledge of the signal which excites the system. It is pointed out that practical blind deconvolution can only be effected iteratively at present. An iterative blind identification algorithm is described and is illustrated by recovery of images from blurred versions contaminated with noise of varying levels. Successful blind deconvolution is achieved without invoking prior knowledge of either the forms or the supports of either the original images or the point spread functions, which respectively correspond to exciting signals and response functions.