Geometric lightguide for near-eye light field displays.

Most near-eye displays with one fixed focal plane suffer from the vergence-accommodation conflict and cause visual discomfort to users. In contrast, light field displays can provide natural and comfortable 3D visual sensation to users without the conflict. This paper presents a near-eye light field display consisting of a geometric lightguide and a light field generator, along with a collimator to ensure the light rays propagating in the lightguide are collimated. Unlike most lightguides, which reduce thickness by employing total internal reflection that can easily generate stray light, our lightguide directly propagates light rays without total internal reflection. The partially reflective mirrors of the lightguide expand the exit pupil to achieve an eyebox of 13m m(h o r i z o n t a l)×6.5m m(v e r t i c a l) with an eye relief of 18 mm. The collimator and the light field generator, both having effective focal lengths different in the horizontal and vertical directions, are designed to provide a 40-deg diagonal field of view. The working range of the light field generator, which is 30 cm to infinity, is verified qualitatively and quantitatively by experiments. We optimize the illuminance uniformity and analyze the illuminance variation across the eyebox. Further, we minimize the ghost artifact (referring to the split-up of light fields replicated by the partially reflective mirrors) by orienting the partially reflective mirrors at slightly different angles to enhance the image quality for short-range applications such as medical surgery.

Efficient and Accurate Stitching for 360° Dual-Fisheye Images and Videos.

Back-to-back dual-fisheye cameras are the most cost-effective devices to capture 360° visual content. However, image and video stitching for such cameras often suffer from the effect of fisheye distortion, photometric inconsistency between the two views, and non-collocated optical centers. In this paper, we present algorithms for geometric calibration, photometric compensation, and seamless stitching to address these issues for back-to-back dual-fisheye cameras. Specifically, we develop a co-centric trajectory model for geometric calibration to characterize both intrinsic and extrinsic parameters of the fisheye camera to fifth-order precision, a photometric correction model for intensity and color compensation to provide efficient and accurate local color transfer, and a mesh deformation model along with an adaptive seam carving method for image stitching to reduce geometric distortion and ensure optimal spatiotemporal alignment. The stitching algorithm and the compensation algorithm can run efficiently for 1920×960 images. Quantitative evaluation of geometric distortion, color discontinuity, jitter, and ghost artifact of the resulting image and video shows that our solution outperforms the state-of-the-art techniques.

Enhancement and Speedup of Photometric Compensation for Projectors by Reducing Inter-Pixel Coupling and Calibration Patterns.

For a procam to preserve the color appearance of an image projected on a color surface, the photometric distortion introduced by the color surface has to be properly compensated. The performance of such photometric compensation relies on an accurate estimation of the projector nonlinearity. In this paper, we improve the accuracy of projector nonlinearity estimation by taking inter-pixel coupling into consideration. In addition, to respond quickly to the change of projection area due to projector movement, we reduce the number of calibration patterns from six to one and use the projected image as the calibration pattern. This greatly improves the computational efficiency of re-calibration that needs to be performed on the fly during a multimedia presentation without breaking its continuity. Both objective and subjective results are provided to illustrate the effectiveness of the proposed method for color compensation.

Light Field Synthesis by Training Deep Network in the Refocused Image Domain.

Light field imaging, which captures spatial-angular information of light incident on image sensors, enables many interesting applications such as image refocusing and augmented reality. However, due to the limited sensor resolution, a trade-off exists between the spatial and angular resolutions. To increase the angular resolution, view synthesis techniques have been adopted to generate new views from existing views. However, traditional learning-based view synthesis mainly considers the image quality of each view of the light field and neglects the quality of the refocused images. In this paper, we propose a new loss function called refocused image error (RIE) to address the issue. The main idea is that the image quality of the synthesized light field should be optimized in the refocused image domain because it is where the light field is viewed. We analyze the behavior of RIE in the spectral domain and test the performance of our approach against previous approaches on both real (INRIA) and software-rendered (HCI) light field datasets using objective assessment metrics such as MSE, MAE, PSNR, SSIM, and GMSD. Experimental results show that the light field generated by our method results in better refocused images than previous methods.

Analysis of Disparity Error for Stereo Autofocus.

As more and more stereo cameras are installed on electronic devices, we are motivated to investigate how to leverage disparity information for autofocus. The main challenge is that stereo images captured for disparity estimation are subject to defocus blur unless the lenses of the stereo cameras are at the in-focus position. Therefore, it is important to investigate how the presence of defocus blur would affect stereo matching and, in turn, the performance of disparity estimation. In this paper, we give an analytical treatment of this fundamental issue of disparity-based autofocus by examining the relation between image sharpness and disparity error. A statistical approach that treats the disparity estimate as a random variable is developed. Our analysis provides a theoretical backbone for the empirical observation that, regardless of the initial lens position, disparity-based autofocus can bring the lens to the hill zone of the focus profile in one movement. The insight gained from the analysis is useful for the implementation of an autofocus system.

Enhancement of backlight-scaled images.

Switching the liquid crystal display (LCD) backlight of a portable multimedia device to a low power level saves energy but results in poor image quality especially for the low-luminance image areas. In this paper, we propose an image enhancement algorithm that overcomes such effects of dim LCD backlight by taking the human visual property into consideration. It boosts the luminance of image areas below the perceptual threshold while preserving the contrast of the other image areas. We apply the just noticeable difference theory and decompose an image into an HVS response layer and a background luminance layer. The boosting and compression processes, which enhance the visibility of the low-luminance image areas, are carried out in the background luminance layer to avoid luminance gradient reversal and over-compensation. The contrast of the processed image is further enhanced by exploiting the Craik-O'Brein-Cornsweet visual illusion. Experimental results are provided to show the performance of the proposed algorithm.