Dataset Information

Defocus blur discrimination in natural images with natural optics.

ABSTRACT: The lens system in the human eye is able to best focus light from only one distance at a time.Therefore, many objects in the natural environment are not imaged sharply on the retina. Furthermore, light from objects in the environment is subject to the particular aberrations of the observer's lens system (e.g., astigmatism and chromatic aberration). We refer to blur created by the observer's optics as "natural" or "defocus" blur as opposed to "on-screen" blur created by software on a display screen. Although blur discrimination has been studied extensively, human ability to discriminate defocus blur in images of natural scenes has not been systematically investigated. Here, we measured discrimination of defocus blur for a collection of natural image patches, sampled from well-focused photographs. We constructed a rig capable of presenting stimuli at three physical distances simultaneously. In Experiment 1, subjects viewed monocularly two simultaneously presented natural image patches through a 4-mm artificial pupil at ±1° eccentricity. The task was to identify the sharper patch. Discrimination thresholds varied substantially between stimuli but were correlated between subjects. The lowest thresholds were at or below the lowest thresholds ever reported. In a second experiment, we paralyzed accommodation and retested a subset of conditions from Experiment 1. A third experiment showed that removing contrast as a cue to defocus blur had only a modest effect on thresholds. Finally, we describe a simple masking model and evaluate how well it can explain our experimental results and the results from previous blur discrimination experiments.

SUBMITTER: Sebastian S

PROVIDER: S-EPMC4419382 | biostudies-other | 2015

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Defocus blur discrimination in natural images with natural optics.

Sebastian Stephen S Burge Johannes J Geisler Wilson S WS

Journal of vision 20150101 5

The lens system in the human eye is able to best focus light from only one distance at a time.Therefore, many objects in the natural environment are not imaged sharply on the retina. Furthermore, light from objects in the environment is subject to the particular aberrations of the observer's lens system (e.g., astigmatism and chromatic aberration). We refer to blur created by the observer's optics as "natural" or "defocus" blur as opposed to "on-screen" blur created by software on a display scre ...[more]

PMID: 26067534

Similar Datasets

Project description:Adaptive optics optical coherence tomography (AO-OCT) technology enables non-invasive, high-resolution three-dimensional (3D) imaging of the retina and promises earlier detection of ocular disease. However, AO-OCT data are corrupted by eye-movement artifacts that must be removed in post-processing, a process rendered time-consuming by the immense quantity of data. To efficiently remove eye-movement artifacts at the level of individual A-lines, including those present in any individual reference volume. We developed a registration method that cascades (1) a 3D B-scan registration algorithm with (2) a global A-line registration algorithm for correcting torsional eye movements and image scaling and generating global motion-free coordinates. The first algorithm corrects 3D translational eye movements to a single reference volume, accelerated using parallel computing. The second algorithm combines outputs of multiple runs of the first algorithm using different reference volumes followed by an affine transformation, permitting registration of all images to a global coordinate system at the level of individual A-lines. The 3D B-scan algorithm estimates and corrects 3D translational motions with high registration accuracy and robustness, even for volumes containing microsaccades. Averaging registered volumes improves our image quality metrics up to 22 dB. Implementation in CUDA™ on a graphics processing unit registers a 512 × 512 × 512 volume in only 10.6 s, 150 times faster than MATLAB™ on a central processing unit. The global A-line algorithm minimizes image distortion, improves regularity of the cone photoreceptor mosaic, and supports enhanced visualization of low-contrast retinal cellular features. Averaging registered volumes improves our image quality up to 9.4 dB. It also permits extending the imaging field of view (∼2.1 × ) and depth of focus (∼5.6 × ) beyond what is attainable with single-reference registration. We can efficiently correct eye motion in all 3D at the level of individual A-lines using a global coordinate system.

Dataset Information

Defocus blur discrimination in natural images with natural optics.

Publications

Defocus blur discrimination in natural images with natural optics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure