We propose a public-key-based optical image cryptosystem for practical secure communications since conventional optical cryptosystems that use a symmetric algorithm are confronted by the problem of key delivery. The proposed system employs a hybrid architecture in which a double-random-phase encoding is used to cipher and decipher an image and an asymmetric algorithm is used for ciphering and deciphering the session key. To solve this problem of key delivery, the double-random-phase encoding algorithm is analyzed to establish a covert channel and therefore elucidate the phenomenon that the amplitude part of the ciphered image is less sensitive to quantization error than the other parts in the Fourier and output planes. The session key is then hidden in the covert channel setup in the ciphered image and extracted at the receiver side. Experimental results demonstrate that the amplitude parts in the Fourier and output planes are better suited to convey the session key because they enable reconstructed images with a higher visual quality to be obtained.

In this paper, we propose a new depth image-based rending (DIBR) scheme for 3DTV applications, where a background sprite model is utilized for dis-occlusion/hole filling purpose. Dissimilar to traditional spatial (e.g., interpolation or inpainting) and temporal methods, our algorithm is capable of recovering the holes with true background information by incrementally integrating the spatial and temporal information of the video in a unified background sprite model. The technique of background sprite model construction in this paper is featured of resolving camera motions existing in most of the consumer videos, accurate registration of multi-frame information, and efficient memory use and computation in realistic 3DTV application. To register/stitch each input frame to the background sprite model accurately, foreground removal considering color and depth information and an adaptive key-frame-based scheme are developed for transform computation. Experimental results show that our proposed scheme has a large temporal reference distance and can retrieve true background information accurately, thus leading to better quality after novel view synthesis compared to existing spatial or spatio-temporal algorithms, especially for videos with significant camera motions or complex backgrounds.

In this paper, an efficient instance segmentation scheme based on deep convolutional neural networks is proposed to deal with unconstrained psoriasis images for computer-aided diagnosis. To achieve instance segmentation, the You Only Look At CoefficienTs (YOLACT) network composed of backbone, feature pyramid network (FPN), Protonet, and prediction head is used to deal with psoriasis images. The backbone network is used to extract feature maps from an image, and FPN is designed to generate multiscale feature maps for effectively classifying and localizing objects with multiple sizes. The prediction head is used to predict the classification information, bounding box information, and mask coefficients of objects. Some prototypes generated by Protonet are combined with mask coefficients to estimate the pixel-level shapes for objects. To achieve instance segmentation for unconstrained psoriasis images, YOLACT++ with a pretrained model is retrained via transfer learning. To evaluate the performance of the proposed scheme, unconstrained psoriasis images with different severity levels are collected for testing. As for subjective testing, the psoriasis regions and normal skin areas can be located and classified well. The four performance indices of the proposed scheme were higher than 93% after cross validation. About object localization, the Mean Average Precision (mAP) rates of the proposed scheme were at least 85.9% after cross validation. As for efficiency, the frames per second (FPS) rate of the proposed scheme reached up to 15. In addition, the F1_score and the execution speed of the proposed scheme were higher than those of the Mask Region-Based Convolutional Neural Networks (R-CNN)-based method. These results show that the proposed scheme based on YOLACT++ can not only detect psoriasis regions but also distinguish psoriasis pixels from background and normal skin pixels well. Furthermore, the proposed instance segmentation scheme outperforms the Mask R-CNN-based method for unconstrained psoriasis images.

In this paper, a keyword detection scheme is proposed based on deep convolutional neural networks for personal information protection in document images. The proposed scheme is composed of key character detection and lexicon analysis. The first part is the key character detection developed based on RetinaNet and transfer learning. To find the key characters, RetinaNet, which is composed of convolutional layers featuring a pyramid network and two subnets, is exploited to detect key characters within the region of interest in a document image. After the key character detection, the second part is a lexicon analysis, which analyzes and combines several key characters to find the keywords. To train the model of RetinaNet, synthetic image generation and data augmentation are exploited to yield a large image dataset. To evaluate the proposed scheme, many document images are selected for testing, and two performance measurements, IoU (Intersection Over Union) and mAP (Mean Average Precision), are used in this paper. Experimental results show that the mAP rates of the proposed scheme are 85.1% and 85.84% for key character detection and keyword detection, respectively. Furthermore, the proposed scheme is superior to Tesseract OCR (Optical Character Recognition) software for detecting the key characters in document images. The experimental results demonstrate that the proposed method can effectively localize and recognize these keywords within noisy document images with Mandarin Chinese words.