Digital holography is mainly used today for metrology and microscopic imaging and is emerging as an important potential technology for future holographic television. To generate the holographic content, computer-generated holography (CGH) techniques convert geometric descriptions of a 3D scene content. To model different surface types, an accurate model of light propagation has to be considered, including for example, specular and diffuse reflection. In previous work, we proposed a fast CGH method for point cloud data using multiple wavefront recording planes, look-up tables (LUTs) and occlusion processing. This work extends our method to account for diffuse reflections, enabling rendering of deep 3D scenes in high resolution with wide viewing angle support. This is achieved by modifying the spectral response of the light propagation kernels contained by the look-up tables. However, holograms encoding diffuse reflective surfaces depict significant amounts of speckle noise, a problem inherent to holography. Hence, techniques to improve the reduce speckle noise are evaluated in this paper. Moreover, we propose as well a technique to suppress the aperture diffraction during numerical, viewdependent rendering by apodizing the hologram. Results are compared visually and in terms of their respective computational efficiency. The experiments show that by modelling diffuse reflection in the LUTs, a more realistic yet computationally efficient framework for generating high-resolution CGH is achieved.
Recently several papers reported efficient techniques to compress digital holograms. Typically, the rate-distortion performance of these solutions was evaluated by means of objective metrics such as Peak Signal-to-Noise Ratio (PSNR) or the Structural Similarity Index Measure (SSIM) by either evaluating the quality of the decoded hologram or the reconstructed compressed hologram. Seen the specific nature of holograms, it is relevant to question to what extend these metrics provide information on the effective visual quality of the reconstructed hologram. Given that today no holographic display technology is available that would allow for a proper subjective evaluation experiment, we propose in this paper a methodology that is based on assessing the quality of a reconstructed compressed hologram on a regular 2D display. In parallel, we also evaluate several coding engines, namely JPEG configured with the default perceptual quantization tables and with uniform quantization tables, JPEG 2000, JPEG 2000 extended with arbitrary packet decompositions and direction-adaptive filters and H.265/HEVC configured in intra-frame mode. The experimental results indicate that the perceived visual quality and the objective measures are well correlated. Moreover, also the superiority of the HEVC and the extended JPEG 2000 coding engines was confirmed, particularly at lower bitrates.
This paper presents a new iterative motion correction technique composed of motion estimation in projection space, motion segmentation in image space, and motion compensation within an analytical filtered-backprojection (FBP) image reconstruction algorithm. The motion is estimated by elastic registration of acquired projections on reference projections. Reference projections are sampled from the image, reconstructed in a previous iteration step. To apply the motion compensation locally, the image regions significantly affected by motion are segmented. First the perceived motion is identified in projection space by computing the absolute difference between acquired line integrals and reference line integrals. Then, differences are reconstructed in image space, and the image is regularized with a pipeline of standard image processing operators. The result of this procedure is a normalized motion map, associating each image element with a measure of the local motion detected there. The estimated displacement vectors in projection space and the reconstructed motion map in image space are then used by an adaptive motion-compensated FBP algorithm to reconstruct a sharper image. Results are shown qualitatively and quantitatively for reconstructions from realistic projections, simulated from clinical patient data. Since the method does not assume any periodicity of the motion model, it can correct reconstruction artifacts due to unstructured patient motion, such as breath-hold failure, abdominal contractions, and nervous movements.
This paper presents an iterative method for compensation of motion artifacts for slowly rotating computed tomography (CT) systems. The inconsistencies among projections introduce severe reconstruction artifacts for free breathing acquisitions. Streaks and false structures appear and the resolution is limited by strong blurring. The rationale of the motion compensation method is to iteratively correct the reconstructed image by first extracting the motion artifacts in projection space, then reconstructing the artifacts in image space, and finally subtracting the artifacts from the original reconstruction. The perceived motion is extracted in projection space from the difference between acquired and reference projections, sampled from the image reconstructed in a previous iteration step. The initial image is reconstructed from acquired data and is nevertheless considered as the reference, although it contains artifacts. This image is iteratively corrected by subtraction of the estimated motion artifacts. The originality of the technique stems from the fact that the patient motion is not estimated but the artifacts are reconstructed in image space. It can provide sharp static anatomical images on slowly rotating on-board imagers in radiotherapy or interventional C-arm systems. Qualitative and quantitative figures are shown for experiments based on simulated projections of a sequence of clinical images resulting from a respiratory-gated helical CT acquisition. The border of the diaphragm becomes sharper and the contrast improves for small structures in the lungs.