Hardware-assisted lifetime confidentiality of unmodified libraries is required to protect proprietary IP from potentially compromised privileged software. Previous research, however, has found that they do not provide it with smaller TCB size to unmodified binaries. This paper proposes SGX-Capsule, a confidential execution engine that executes a native library within an SGX enclave while the library remains confidential at rest and in transit. Reduced TCB size by accommodating the target library only and interfacing with others across the enclave boundary decreases possible vulnerabilities and performance overhead from memory overcommitment.

An unsupervised video object segmentation algorithm, which discovers a primary object in a video sequence automatically, is proposed in this work. We introduce three energies in terms of foreground and background probability distributions: Markov, spatiotemporal, and antagonistic energies. Then, we minimize a hybrid of the three energies to separate a primary object from its background. However, the hybrid energy is nonconvex. Therefore, we develop the alternate convex optimization (ACO) scheme, which decomposes the nonconvex optimization into two quadratic programs. Moreover, we propose the forward-backward strategy, which performs the segmentation sequentially from the first to the last frames and then vice versa, to exploit temporal correlations. Experimental results on extensive datasets demonstrate that the proposed ACO algorithm outperforms the state-of-the-art techniques significantly.

A genetic programming (GP)-based framework to learn the effective feature representation for image dehazing is proposed in this work. In GP, an individual program is randomly generated and genetically evolved to achieve the desired goal. To make GP estimate haze in an input image, a set of operators and operands is designed, each of which is a primitive of a GP program. Specifically, we provide four basic features as candidates, and also include function operators to construct sophisticated representations of these features. After the entire GP process finishes, we obtain a near-optimal compact descriptor for haze estimation. Experimental results demonstrate that the proposed algorithm enhances the visual quality of haze-degraded images both objectively and subjectively.

A graph-based system to simulate the movements and interactions of multiple random walkers (MRW) is proposed in this work. In the MRW system, multiple agents traverse a single graph simultaneously. To achieve desired interactions among those agents, a restart rule can be designed, which determines the restart distribution of each agent according to the probability distributions of all agents. In particular, we develop the repulsive rule for data clustering. We illustrate that the MRW clustering can segment real images reliably. Furthermore, we propose a novel image cosegmentation algorithm based on the MRW clustering. Specifically, the proposed algorithm consists of two steps: inter-image concurrence computation and intra-image MRW clustering. Experimental results demonstrate that the proposed algorithm provides promising cosegmentation performance.

A dark image enhancement algorithm based on the pairwise target contrast and the multi-scale detail boosting is proposed in this work. We first compute the pairwise target contrast between a pair of pixels, which represents the desired gray level difference of the two pixels in the output image. By aggregating the pairwise target contrasts for all pairs of pixels in an image, we formulate a cost function for the enhancement. By minimizing the cost function, we obtain the optimal transformation function and enhance the image. In addition, we propose a multi-scale approach to boost details in the globally enhanced image. Experimental results show that the proposed algorithm enhances the contrast and visibility of dark images more effectively than conventional algorithms.

We propose a video stabilization algorithm, which extracts a guaranteed number of reliable feature trajectories for robust mesh grid warping. We first estimate feature trajectories through a video sequence and transform the feature positions into rolling-free smoothed positions. When the number of the estimated trajectories is insufficient, we generate virtual trajectories by augmenting incomplete trajectories using a low-rank matrix completion scheme. Next, we detect feature points on a large moving object and exclude them so as to stabilize camera movements, rather than object movements. With the selected feature points, we set a mesh grid on each frame and warp each grid cell by moving the original feature positions to the smoothed ones. For robust warping, we formulate a cost function based on the reliability weights of each feature point and each grid cell. The cost function consists of a data term, a structure-preserving term, and a regularization term. By minimizing the cost function, we determine the robust mesh grid warping and achieve the stabilization. Experimental results demonstrate that the proposed algorithm reconstructs videos more stably than the conventional algorithms.

A novel contrast enhancement (CE) algorithm using 2-dimensional (2D) histograms, which transforms pixel values adaptively based on the depth information, is proposed in this work. In general, foreground objects convey more important visual information than background regions. Hence we assign high CE priorities to foreground pixels using the depth values and generate a depth-guided 2D histogram. Then, we stretch the gray-level differences of adjacent foreground pixels more strongly than those of adjacent background pixels. Moreover, to enhance background regions as well, we design two transformation functions for the foreground and the background separately. By combining the two functions according to pixel depths, we obtain an adaptive space-variant transformation function, which is finally used to reconstruct the output image. Experimental results show that the proposed algorithm outperforms conventional CE algorithms by enhancing salient foreground objects efficiently and preserving background details faithfully.

A video genre classification algorithm based on the voting from multiple SVMs is proposed in this work. While conventional genre classifiers use generic baseline features, we employ more specialized features to describe five video genres: animation, commercial, entertainment, drama, and sports. We also present a robust classification algorithm using multiple SVMs, which consider all possible binary grouping of the five genres. Given a query video, each SVM casts a probabilistic vote for each genre. Then, the optimal genre with the maximum votes is selected. Experimental results show that the proposed algorithm provides more accurate classification performance than conventional algorithms.

An optimized brightness-compensated contrast enhancement (BCCE) algorithm for transmissive liquid crystal displays (LCDs) is proposed in this paper. We first develop a global contrast enhancement scheme to compensate for the reduced brightness when the backlight of an LCD device is dimmed for power reduction. We also derive a distortion model to describe the information loss due to the brightness compensation. Then, we formulate an objective function that consists of the contrast enhancement term and the distortion term. By minimizing the objective function, we maximize the backlight-scaled image contrast, subject to the constraint on the distortion. Simulation results show that the proposed BCCE algorithm provides high-quality images, even when the backlight intensity is reduced by up to 50-70% to save power.

A novel contrast enhancement algorithm based on the layered difference representation of 2D histograms is proposed in this paper. We attempt to enhance image contrast by amplifying the gray-level differences between adjacent pixels. To this end, we obtain the 2D histogram h(k, k + l ) from an input image, which counts the pairs of adjacent pixels with gray-levels k and k + l , and represent the gray-level differences in a tree-like layered structure. Then, we formulate a constrained optimization problem based on the observation that the gray-level differences, occurring more frequently in the input image, should be more emphasized in the output image. We first solve the optimization problem to derive the transformation function at each layer. We then combine the transformation functions at all layers into the unified transformation function, which is used to map input gray-levels to output gray-levels. Experimental results demonstrate that the proposed algorithm enhances images efficiently in terms of both objective quality and subjective quality.

We propose a novel backlight-scaled contrast enhancement algorithm to maintain the image quality when the backlight of a TFT-LCD display is dimmed for power reduction. First, we derive the transformation function to maintain the perceived luminance and enhance the contrast. Second, we model the perceptual distortion, which describes the information loss due to the backlight scaling. Then, we propose a Lagrangian method to maximize the backlight-scaled image contrast subject to the constraint on the perceptual distortion. Simulation results show that the proposed algorithm provides better image qualities than the conventional algorithms.

A novel contrast enhancement algorithm based on the layered difference representation is proposed in this work. We first represent gray-level differences at multiple layers in a tree-like structure. Then, based on the observation that gray-level differences, occurring more frequently in the input image, should be more emphasized in the output image, we solve a constrained optimization problem to derive the transformation function at each layer. Finally, we aggregate the transformation functions at all layers into the overall transformation function. Simulation results demonstrate that the proposed algorithm enhances images efficiently in terms of both objective quality and subjective quality.

A power-constrained contrast-enhancement algorithm for emissive displays based on histogram equalization (HE) is proposed in this paper. We first propose a log-based histogram modification scheme to reduce overstretching artifacts of the conventional HE technique. Then, we develop a power-consumption model for emissive displays and formulate an objective function that consists of the histogram-equalizing term and the power term. By minimizing the objective function based on the convex optimization theory, the proposed algorithm achieves contrast enhancement and power saving simultaneously. Moreover, we extend the proposed algorithm to enhance video sequences, as well as still images. Simulation results demonstrate that the proposed algorithm can reduce power consumption significantly while improving image contrast and perceptual quality.

A nonlocal minimum mean square error (MMSE) image denoising algorithm to remove Poisson noise is proposed in this work. Based on the Bayesian estimation theory, we first derive the nonlocal MMSE denoising filter, which can minimize the mean square error (MSE) of a denoised block. Then, we develop an approximation of the filter for practical implementation. Simulation results show that the proposed algorithm provides significantly better denoising performance than the conventional nonlocal means filter and its recent extension for Poisson noise.

A novel contrast enhancement algorithm with histogram-guided boundary conditions is proposed in this work. The proposed algorithm enhances details in local regions by boosting gradient components, while improving the overall contrast by imposing boundary conditions based on a global transformation function. Moreover, we develop an efficient masking scheme, called the soft masking, to strike the balance between the global enhancement and the local enhancement. Simulation results demonstrate that the proposed algorithm can yield high quality output images by improving both global and local contrast simultaneously.

A novel power-constrained contrast enhancement algorithm for organic light-emitting diode (OLED) displays is proposed in this work. We first develop the log-modified histogram equalization (LMHE) scheme, which reduces overstretching artifacts of the conventional histogram equalization technique. Then, we model the power consumption in OLED displays, and incorporate it into LMHE to achieve the optimal tradeoff between contrast enhancement and power saving. Simulation results demonstrate that the proposed algorithm can reduce the power consumption significantly, while preserving image qualities.

This paper proposes a new actuator system using a movable pulley for a bio-mimetic system and wearable robot applications, which is able to store energy in advance and to release it in such a way to satisfy desired speed and force control performances, simultaneously. Since new actuator system consists of two motors (dual-motors), a movable pulley, and series spring connection for high speed/force operation characteristics, it is referred to as Dual-motor system using a Movable Pulley with a Series Elastic Actuator (DuMP-SEA) in this paper. Also, the proposed actuator system not only combines individual speed/force operation region of each motor, but also extends the combined operation region by using the energy stored in advance. Finally, we suggest the extended operation region (high speed and high force characteristics) obtained by using the DuMP-SEA through simulations and experiments.