Enhancing retinal images by nonlinear registration

Being able to image the human retina in high resolution opens a new era in many important fields, such as pharmacological research for retinal diseases, researches in human cognition, nervous system, metabolism and blood stream, to name a few. In this paper, we propose to share the knowledge acquired in the fields of optics and imaging in solar astrophysics in order to improve the retinal imaging at very high spatial resolution in the perspective to perform a medical diagnosis. The main purpose would be to assist health care practitioners by enhancing retinal images and detect abnormal features. We apply a nonlinear registration method using local correlation tracking to increase the field of view and follow structure evolutions using correlation techniques borrowed from solar astronomy technique expertise. Another purpose is to define the tracer of movements after analyzing local correlations to follow the proper motions of an image from one moment to another, such as changes in optical flows that would be of high interest in a medical diagnosis.Comment: 21 pages, 7 figures, submitted to Optics Communication

Image blur estimation based on the average cone of ratio in the wavelet domain

In this paper, we propose a new algorithm for objective blur estimation using wavelet decomposition. The central idea of our method is to estimate blur as a function of the center of gravity of the average cone ratio (ACR) histogram. The key properties of ACR are twofold: it is powerful in estimating local edge regularity, and it is nearly insensitive to noise. We use these properties to estimate the blurriness of the image, irrespective of the level of noise. In particular, the center of gravity of the ACR histogram is a blur metric. The method is applicable both in case where the reference image is available and when there is no reference. The results demonstrate a consistent performance of the proposed metric for a wide class of natural images and in a wide range of out of focus blurriness. Moreover, the proposed method shows a remarkable insensitivity to noise compared to other wavelet domain methods

A No Reference Objective Color Image Sharpness Metric

International audienceIn this work, we propose a no reference color image quality assessment metric. The proposed metric makes use of a wavelet-based multiscale structure tensor [1] as an extension of the single-scale structure tensor proposed by Di Zenzo [15]. The multiscale structure tensor allows for accumulating multiscale gradient information of local regions of the color image. Thus, averaging properties are maintained while preserving edge structure. This structure tensor is capable of identifying edges in spite of the presence of noise. Once edges are identified, we define a sharpness metric based on the eigenvalues of the multiscale structure tensor. Particularly, we show that the difference of the eigenvalues of the multiscale structure tensor can be used to measure the sharpness of color edges. Based on this fact we formulate our no reference sharpness metric for color images. Experiments performed on LIVE database indicate that the objective scores obtained by the proposed metric agree well with the subjective assessment score

AN EFFICIENT NO-REFERENCE METRIC FOR PERCEIVED BLUR

International audienceThis paper presents an efficient no-reference metric that quantifies perceived image quality induced by blur. Instead of explicitly simulating the human visual perception of blur, it calculates the local edge blur in a cost-effective way, and applies an adaptive neural network to empirically learn the highly nonlinear relationship between the local values and the overall image quality. Evaluation of the proposed metric using the LIVE blur database shows its high prediction accuracy at a largely reduced computational cost. To further validate the performance of the blur metric on its robustness against different image content, two additional quality perception experiments were conducted: one with highly textured natural images and one with images with an intentionally blurred background . Experimental results demonstrate that the proposed blur metric is promising for real-world applications both in terms of computational efficiency and practical reliability

Evaluation of Perceptual Contrast and Sharpness Measures for Meteorological Satellite Images

AbstractSharpness and contrast have great impact on perceived quality of an image. This paper focuses on sharpness and contrast measures to evaluate quality of Thermal Infrared (TIR1) channel of Indian National Satellite-3D (INSAT-3D) without using any reference image. Most of the sharpness metrics can scarcely manage to discern image quality degradation against high frequency behavior due to noise. Six Image Quality Measures (IQMs) are employed to study their behavior in terms of blur, noise and intensity changes simultaneously. Results show that (1) change in value of Measure Of Enhancement By Entropy (EMEE) is more discernible with change in contrast of an INSAT-3D image as compared to other measures and (2) Second Derivative Like Measure Of Enhancement (SDME) has a potential to distinguish high frequency content due to sharpness arisen due to un estimated noise up to some remarkable level in case of TIR1 INSAT-3D satellite images. Performance comparison of six measures against blur, noise, contrast and sharpness changes is presented

Automatic Video Quality Measurement System And Method Based On Spatial-temporal Coherence Metrics

An automatic video quality (AVQ) metric system for evaluating the quality of processed video and deriving an estimate of a subjectively determined function called Mean Time Between Failures (MTBF). The AVQ system has a blockiness metric, a streakiness metric, and a blurriness metric. The blockiness metric can be used to measure compression artifacts in processed video. The streakiness metric can be used to measure network artifacts in the processed video. The blurriness metric can measure the degradation (i.e., blurriness) of the images in the processed video to detect compression artifacts.Georgia Tech Research Corporatio