Microlens Images Research Articles

Scalable Coding of Plenoptic Images by Using a Sparse Set and Disparities.

One of the light field capturing techniques is the focused plenoptic capturing. By placing a microlens array in front of the photosensor, the focused plenoptic cameras capture both spatial and angular information of a scene in each microlens image and across microlens images. The capturing results in a significant amount of redundant information, and the captured image is usually of a large resolution. A coding scheme that removes the redundancy before coding can be of advantage for efficient compression, transmission, and rendering. In this paper, we propose a lossy coding scheme to efficiently represent plenoptic images. The format contains a sparse image set and its associated disparities. The reconstruction is performed by disparity-based interpolation and inpainting, and the reconstructed image is later employed as a prediction reference for the coding of the full plenoptic image. As an outcome of the representation, the proposed scheme inherits a scalable structure with three layers. The results show that plenoptic images are compressed efficiently with over 60 percent bit rate reduction compared with High Efficiency Video Coding intra coding, and with over 20 percent compared with an High Efficiency Video Coding block copying mode.

Focused plenoptic camera and rendering

Plenoptic cameras, constructed with internal microlens arrays, capture both spatial and angular information, i.e., the full 4-D radiance, of a scene. The design of traditional plenoptic cameras assumes that each microlens image is completely defocused with respect to the image created by the main camera lens. As a result, only a single pixel in the final image is rendered from each microlens image, resulting in disappointingly low resolution. A recently developed alternative approach based on the focused plenoptic camera uses the microlens array as an imaging system focused on the image plane of the main camera lens. The flexible spatioangular trade-off that becomes available with this design enables rendering of final images with significantly higher resolution than those from traditional plenoptic cameras. We analyze the focused plenoptic camera in optical phase space and present basic, blended, and depth-based rendering algorithms for producing high-quality, high-resolution images. We also present our graphics-processing-unit-based implementations of these algorithms, which are able to render full screen refocused images in real time.

Automatic procedure for aberration compensation in digital holographic microscopy and applications to specimen shape compensation

We present a procedure that compensates for phase aberrations in digital holographic microscopy by computing a polynomial phase mask directly from the hologram. The phase-mask parameters are computed automatically without knowledge of physical values such as wave vectors, focal lengths, or distances. This method enables one to reconstruct correct and accurate phase distributions, even in the presence of strong and high-order aberrations. Examples of applications are shown for microlens imaging and for compensating for the deformations associated with a tilted thick plate. Finally we show that this method allows compensation for the curvature of the specimen, revealing its surface defects and roughness. Examples of applications are shown for microlenses and metallic sphere imaging.

Adaptive 3D-DCT compression algorithm for continuous parallax 3D integral imaging

Abstract Integral imaging is employed as part of a three-dimensional imaging system, allowing the display of full colour images with continuous parallax within a wide viewing zone. A significant quantity of data is required to represent a captured integral 3D image with high resolution. A lossy compression scheme has been developed based on the use of a 3D-DCT, which make possible efficient storage and transmission of such images, while maintaining all information necessary to produce a high quality 3D display. In this paper, a novel approach to the problem of compressing the significant quantity of data required to represent integral 3D images is presented. The algorithm is based on using a variable number of microlens images (or sub-images) in the computation of the 3D-DCT. It involves segmentation of the planar mean image formed by the mean values of the microlens images and it takes advantage of the high cross-correlation between the sub-images generated by the microlens array. The algorithm has been simulated on several integral 3D images. It was found that the proposed algorithm improves the rate-distortion performance when compared to baseline JPEG and previously reported 3D-DCT compression scheme with respect to compression ratio and subjective and objective image quality.