A generalized region labeling algorithm for image coding, restoration, and segmentation

Human face detection plays an important role in applications such as video surveillance, human computer interface, face recognition and face image database management. In this paper, we propose a novel facial feature detection algorithm for various face image types, conditions, invariant rotation, and any appearances. There are three main steps. First, Radon transform is used for face angle detection on rotated image. Subsequently, the feature regions are detected using Neural Visual Model (NVM). Finally, using image dilation and Radon transform, the facial features are extracted from the detected regions. Input parameters are obtained from the face characteristics and the positions of facial features not including any intensity informations. Our algorithm is successfully tested with various types of faces which are color images, gray images, binary images, wearing the sunglasses, wearing the scarf, lighting effect, low-quality images, color and sketch images from animated cartoon, rotated face images, and rendered face images.

Underwater laser imaging is of great significance in underwater search and marine science, <i>etc</i>. However, traditional underwater laser imaging is often of poor quality with noises and blurs, moreover, the resolution of the image is also low. In order to obtain clear underwater images with high resolution and quality, here, we have designed a range gated imaging underwater imaging system and realized an image restoration approach. In this paper, based on the introduction to the imaging system and image restoration algorithm, the experiment is established by setting the imaging system under water in the lake to capture the underwater targets. With the proposed underwater image restoration approach, images of high quality could be retrieved which proves that the method is able to identify the target ~10 meters away underwater.

The expectation-maximization (EM) algorithm for maximum-likelihood image recovery is guaranteed to converge, but it converges slowly. Its ordered-subset version (OS-EM) is used widely in tomographic image reconstruction because of its order-of-magnitude acceleration compared with the EM algorithm, but it does not guarantee convergence. Recently the ordered-subset, separable-paraboloidal-surrogate (OS-SPS) algorithm with relaxation has been shown to converge to the optimal point while providing fast convergence. We adapt the relaxed OS-SPS algorithm to the problem of image restoration. Because data acquisition in image restoration is different from that in tomography, we employ a different strategy for choosing subsets, using pixel locations rather than projection angles. Simulation results show that the relaxed OS-SPS algorithm can provide an order-of-magnitude acceleration over the EM algorithm for image restoration. This new algorithm now provides the speed and guaranteed convergence necessary for efficient image restoration.

This paper introduces an expectation-maximization (EM) algorithm for image restoration (deconvolution) based on a penalized likelihood formulated in the wavelet domain. Regularization is achieved by promoting a reconstruction with low-complexity, expressed in the wavelet coefficients, taking advantage of the well known sparsity of wavelet representations. Previous works have investigated wavelet-based restoration but, except for certain special cases, the resulting criteria are solved approximately or require demanding optimization methods. The EM algorithm herein proposed combines the efficient image representation offered by the discrete wavelet transform (DWT) with the diagonalization of the convolution operator obtained in the Fourier domain. Thus, it is a general-purpose approach to wavelet-based image restoration with computational complexity comparable to that of standard wavelet denoising schemes or of frequency domain deconvolution methods. The algorithm alternates between an E-step based on the fast Fourier transform (FFT) and a DWT-based M-step, resulting in an efficient iterative process requiring O(N log N) operations per iteration. The convergence behavior of the algorithm is investigated, and it is shown that under mild conditions the algorithm converges to a globally optimal restoration. Moreover, our new approach performs competitively with, in some cases better than, the best existing methods in benchmark tests.

To improve data transmission efficiency, image compression is a commonly used method with the disadvantage of accompanying image distortion. There are many image restoration (IR) algorithms, and one of the most advanced algorithms is the generative adversarial network (GAN)-based method with a high correlation to the human visual system (HVS). To evaluate the performance of GAN-based IR algorithms, we proposed an ensemble image quality assessment (IQA) called ATDIQA (Auxiliary Transformer with DISTS IQA) to give weights on multiscale features global self-attention transformers and local features of convolutional neural network (CNN) IQA of DISTS. The result not only performed better on the perceptual image processing algorithms (PIPAL) dataset with images by GAN IR algorithms but also has good model generalization over LIVE and TID2013 as traditional distorted image datasets. The ATDIQA ensemble successfully demonstrates its performance with a high correlation with the human judgment score of distorted images.

Aiming at correcting the severe chromatic aberration of the long focal lens, the traditional optical design often requires employing complex structure, introducing special dispersive glasses or even utilizing the hybrid refractive-diffractive imaging method. However, complex structure will bring several drawbacks such as large volume and heavy weight. Special glasses and refractive-diffractive hybrid imaging will greatly increase the cost, which hardly meets the needs of miniaturization and low cost of optical imaging module. Although image restoration algorithm is commonly used to optimize the image quality to a certain extent, the optical design and image restoration process are independent of each other. Therefore, it is difficult to ensure the high resolution of the image while realizing the light weight, small volume and low cost of the optical system simultaneously. Utilizing the computational imaging theory, a simple long-focus optical system design method based on the optical/image co-design is proposed and deomonstrated in this paper. On the basis of the idea of global optimization, the reported approach considers the two independent links of optical design and image restoration as a combination. The imaging quality requirements in the optical system design are relaxed at the front end, and the image restoration algorithm is used to remove the residual aberrations in the back end. This method can not only obtain the same or even higher imaging performance, but also reduce the complexity of the optical system.

In recent years, with the development of computer image processing technology and quantitative metallography, quantitative metallographic analysis and image restoration based on machine vision image processing have attracted extensive attention. To deeply study the special ability of machine vision technology in metallographic image restoration and quantitative analysis, we adopt a model construction method, parameter design method and comparative analysis method to collect samples and analyze the machine vision system. Through quantitative metallographic organization, based on the progress in artificial neural networks and classification algorithms, a set of analysis system equipment and image restoration algorithm was finally established. The experimental results show that the recognition rate of this method is 5.69% higher than that of method 3, and the overall recognition rate is 5.1% higher than that of method 3, reaching 98.65%. It basically proves the superiority of the image classification algorithm in this paper and also indirectly proves that the system can play a role in the restoration and quantitative analysis of metallographic tissue images.

End‐to‐end optimization of metalens and artificial intelligence‐driven image restoration algorithms has recently emerged as a powerful tool for realizing ultra‐compact imaging systems. However, the limited imaging quality of existing approaches remains challenging in meeting the demand for commercial devices due to the severe aberrations exhibited by metalens. These results in highly blurred sensor images, creating substantial challenges for accurate image restoration. In this work, a novel meta‐imager is introduced that overcomes this challenge by employing an aperture‐stop‐integrated metalens and co‐designing it with a computational image restoration network using a fully differentiable optimization framework. The proposed imager physically consists of a single metalens and an aperture stop located on the opposite side of the 1 mm‐thick glass substrate. This configuration effectively alleviates off‐axis aberrations such as coma and astigmatism, facilitating the image restoration process of the deep neural networks. The experimental results present that this scheme features 70° field‐of‐view, for full‐color imaging across the entire visible spectrum. It is believed that this work represents a significant advancement in creating ultra‐compact cameras using nanophotonics.

The automatic recognition of human faces presents a significant challenge to the pattern recognition research community. Typically, human faces are very similar in structure with minor differences from person to person. They are actually within one class of "human face". Furthermore, lighting conditions change, while facial expressions and pose variations further complicate the face recognition task as one of the difficult problems in pattern analysis. This paper proposes a novel concept: namely, that faces can be recognized using a line edge map (LEM). The LEM, a compact face feature, is generated for face coding and recognition. A thorough investigation of the proposed concept is conducted which covers all aspects of human face recognition, i.e. face recognition under (1) controlled/ideal conditions and size variations, (2) varying lighting conditions, (3) varying facial expressions, and (4) varying pose. The system performance is also compared with the eigenface method, one of the best face recognition techniques, and with reported experimental results of other methods. A face pre-filtering technique is proposed to speed up the search process. It is a very encouraging to find that the proposed face recognition technique has performed better than the eigenface method in most of the comparison experiments. This research demonstrates that the LEM, together with the proposed generic line-segment Hausdorff distance measure, provides a new method for face coding and recognition.

The application of information hiding to image compression is investigated to improve compression efficiency for JPEG color images. In the proposed method, entropy-coded DCT coefficients of chrominance components are embedded into DCT coefficients of the luminance component. To recover an image in the face of the degradation caused by compression and embedding, an image restoration method is also applied. Experiments show that the use of both information hiding and image restoration is most effective to improve compression efficiency.

There are many applications for systems coping with the problem of face localization and recognition, e.g. model-based video coding, security systems and mug shot matching. Due to variations in illumination, back-ground, visual angle and facial expressions, the problem of machine face recognition is complex. In this paper we present a robust approach for the extraction of facial regions and features out of color images. First, face candidates are located based on the color and shape information. Then the topographic grey-level relief of facial regions is evaluated to determine the position of facial features as eyes and month. Results are shown for two example scenes.

The authors propose a new method (Vision Point Out System (VPOS)) of the extraction of the hand region and the specification of the finger tips from a color image. This algorithm uses the product of the skin color information and the brightness information. Even if the input image has nail and complex background, this method can extract the correct hand region. They evaluated the effectiveness of this method for some hand images with the complex background by the computer experiments. This algorithm can find the extended finger, and can classify "Janken (Japanese finger guessing game; Gu-Choki-Pa)". The string region pointed out by the finger on the background can be extracted.

A modular approach on an adaptive thresholding method for segmentation of cell regions in bioelectric images with complex lighting environments and background conditions is presented in this paper. Preprocessing steps involve low-pass filtering of the image and local contrast enhancement. This image is then adaptively thresholded which produces a binary image. The binary image consists of cell regions and the edges of a metal electrode that show up as bright spots. A local region based approach is used to distinguish between cell regions and the metal electrode tip that cause bright spots. Regional properties such as area are used to separate the cell regions from the non-cell regions. Special emphasis is given on the detection of twins and triplet cells with the help of watershed transformation, which might have been lost if form-factor alone were to be used as the geometrical descriptor to sepa rate the cell and the non-cell regions. Keywords: bioelectric images, image segmentation, adaptive thresholding, cell segmentation, local contrast enhancement, watershed transformation, template matching

The expectation-maximization (EM) algorithm for maximum-likelihood image recovery converges very slowly. Thus, the ordered subsets EM (OS-EM) algorithm has been widely used in image reconstruction for tomography due to an order-of-magnitude acceleration over the EM algorithm. However, OS-EM is not guaranteed to converge. The recently proposed ordered subsets, separable paraboloidal surrogates (OS-SPS) algorithm with relaxation has been shown to converge to the optimal point while providing fast convergence. In this paper, we develop a relaxed OS-SPS algorithm for image restoration. Because data acquisition is different in image restoration than in tomography, we adapt a different strategy for choosing subsets in image restoration which uses pixel location rather than projection angles. Simulation results show that the order-of-magnitude acceleration of the relaxed OS-SPS algorithm can be achieved in restoration. Thus the speed and the guarantee of the convergence of the OS algorithm is advantageous for image restoration as well.

There is distortion phenomenon in image emerge, transmit and record. Image restoration is a process which recover bad image into original image. When we use genetic algorithm for image restoration, there will be premature problem. The paper discusses a new algorithm for image restoration based on combination of parallel genetic algorithm with Hopfield neural network, take the advantage of parallel GA parameter selection and then use Hopfield NN to train sample efficiently. Experiments demonstrate that this optimization method in this paper will overcome premature problem and run more rapidly, as a result obtain a better recovery image.