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Application of Image Processing Variation Model Based on Network Control Robot Image Transmission and Processing System in Multimedia Enhancement Technology
Application of Image Processing Variation Model Based on Network Control Robot Image Transmission...
Wu, Yanmin;Qi, Jinli
Hindawi Journal of Robotics Volume 2022, Article ID 6991983, 10 pages https://doi.org/10.1155/2022/6991983 Research Article Application of Image Processing Variation Model Based on Network Control Robot Image Transmission and Processing System in Multimedia Enhancement Technology 1 2 Yanmin Wu and Jinli Qi Department of Arti cial Intelligence and Big Data, Chongqing College of Electronic Engineering, Chongqing 401331, China Department of General Education and International Studies, Chongqing College of Electronic Engineering, Chongqing 401331, China Correspondence should be addressed to Yanmin Wu; email@example.com Received 20 July 2022; Revised 30 August 2022; Accepted 17 September 2022; Published 29 September 2022 Academic Editor: Shahid Hussain Copyright © 2022 Yanmin Wu and Jinli Qi. �is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. �e rapid development of the information age brings convenience to human life, but it also brings great challenges to information processing technology. Multimedia enhancement technology is an organic combination of multimedia technology and infor- mation processing technology, and it is also an important way of modern multimedia image information processing. However, its usefulness and e†ectiveness are increasingly negatively a†ected by the open information environment. �e processing e†ect is also unable to meet the development requirements of the visual ‡eld. In order to improve this problem, this paper studied the image transmission and processing system of network-controlled robot on the basis of analyzing the characteristics of the problems existing in the current stage of multimedia technology. On this basis, a new image processing variational model was established and applied to multimedia enhancement technology, which improved the eŒciency and e†ect of image information processing. Finally, the feasibility of its application function and performance was tested by experiments. �e test results showed that in the diŒcult mode of the image processing task, the refresh time of the model in this paper in the multimedia enhancement technology was 1.13 s in total, which was not much di†erent from the test results in the easy mode. Also, in the load stress test, the comprehensive test values under full-function operation and partial-function operation were 42.04% and 20.92%, respectively. Compared with the traditional model, the model in this paper has stronger carrying capacity in multimedia enhancement technology and has better processing ability and stability. brought great challenges to the current multimedia en- 1. Introduction hancement technology. When there is a lot of complicated With the maturity of Internet technology and the wide- and dazzling multimedia image information, how to com- press it to reduce the transmission amount and improve the spread use of mobile communications, the image infor- mation used and circulated in the online environment has transmission and processing eŒciency on the basis of shown a rapid development trend. As an important support retaining the e†ective information is a problem that needs to for image information processing, multimedia enhancement be solved by the current multimedia enhancement tech- technology can e†ectively process various types of multi- nology. In the context of the dual development of control media information, such as text, data, video, and voice. technology and computer network, network-controlled However, in the whole complex communication network robots have been maturely applied. Also, its image trans- environment, image information has strong redundancy. All mission and processing system ensures the convenience and kinds of image content, whether in the process of storage, reliability of image processing. �e image processing vari- transmission, or in the process of secondary analysis, have ational model established under this system can ensure that 2 Journal of Robotics estimation were uniﬁed into a variational optimization the key information of the image is not lost. *e reliability and authenticity of the image are improved, which is of great problem . *e image processing variational model of the network-controlled robot image transmission and pro- signiﬁcance for improving the practical application value of multimedia enhancement technology. cessing system has high practicability. However, the appli- As an important means of comprehensive processing of cation in multimedia enhancement technology has not been media information, multimedia enhancement technology eﬀectively applied. In order to enhance the application value has always been the research direction of many scholars. Jan of image processing variational model in multimedia en- et al. proposed a histogram-based energy saving algorithm to hancement technology and realize two-way development, its improve the resolution and image quality of modern mul- application research is very important. Based on the network-controlled robot image trans- timedia devices . Zhang and Huo proposed a multimedia enhancement technique with quantum chaotic graph, which mission and processing system, this paper constructed a new image processing variational model, which was applied to encrypted the image and improved the reliability and se- curity of multimedia images . To achieve the best mul- the multimedia enhancement technology. *rough the ap- plication test, it can be seen that the image processing timedia enhancement layer compression eﬃciency, Hoangvan et al. proposed a novel HEVC-based framework function of the model in this paper was ideal. *e total with high-quality scalability . Ahmed et al. proposed a refresh time in mode 1 was 0.9s, and the refresh time in method for multisaliency enhancement for multimedia mode 4 was 1.13s. In the performance test, the variational image location estimation, which was mainly performed by model in this paper had better spatial adaptability. *e mean-shift clustering of visual words and saliency maps of memory usage had been stable in the range of 38.4%, and the images . Ravisankar et al. used multiresolution sharpened CPU usage had only ﬂuctuated in the range of 6.9%. In the load capacity test, the comprehensive value of the model images for reconstruction enhancement. Also, it was proved by performance measurement that this unsharp masking under the full-function operation of multimedia enhance- ment technology was 42.04%, while the comprehensive value method outperformed other enhancement techniques . Heindel et al. proposed a lossy-to-lossless scalable multi- of the test under partial-function operation was 20.92%. *e load capacity of the model is lower than that of the tradi- media coding system. Scalability was achieved using lossy base layers combined with lossless compression of recon- tional model, but the load capacity is higher. From this point struction errors in enhancement layers . *e development of view, the model constructed in this paper had ideal of image processing requires a higher level of multimedia practicability in the application of multimedia enhancement enhancement technology, and the previous research technology. methods are still lacking in eﬃciency. *e image processing variational model based on the network control robot image 2. Application of Image Processing transmission and processing system can play a strong ad- Variation Model vantage in its application. At present, the variational model in the network-con- 2.1. Multimedia Enhancement Technology. Multimedia en- trolled robot image transmission and processing system has hancement technology is an emerging technology developed been widely used in many ﬁelds. Zhang et al. used a vari- on the basis of the original multimedia technology. It can be ational model for network-controlled robotic image pro- regarded as a small embedded technology system integrating cessing in the linearization enhancement of elastic image hardware and software . *is technology requires the co- denoising models. Simple complexity analysis was also design of software and hardware, so that the hardware and performed . Sridevi and Srinivas Kumar eﬃciently software components can be divided reasonably. In multi- characterized intensity changes in images using a robust media enhancement techniques, nodes can be distributed in image processing variational model based on fractional or around a perceptual image object in a number of diﬀerent nonlinear diﬀusion driven by diﬀerential curvature of the ways, such as random distribution and artiﬁcial positioning. image processing system . Feng proposed a new varia- *ese nodes form technology networks, often through self- tional model under a network-controlled robot image organization. In terms of network functions, each node not transmission and processing system, which was used for only takes on the dual roles of terminal and router of tra- anti-noise document image binarization. Also, the noise and ditional multimedia network nodes but also performs image illumination robustness of the method was veriﬁed . Li data acquisition, storage, management, and fusion . In and Yang proposed adaptive variational functionals for special cases, it is also necessary to cooperate with other image inpainting, which demonstrated a stable variational nodes. *e sink node not only needs to have the function of scheme based on image processing systems. *e existence controlling other devices but also needs to have the functions and uniqueness of minimizing functional solutions were also of establishing a routing table for routing selection, infor- investigated . Combining the advantages of recently mation forwarding, data fusion, and storing image infor- developed robotic image transmission and processing sys- mation of other nodes. Its technical framework is shown in tems, Liu proposed a new variational model for solving the Figure 1. challenging problem of cartoon texture image decomposi- A large number of nodes are distributed in or around the tion . To maximize adaptability, Suwanwimolkul et al. monitoring area in Figure 1. *ese nodes consist of self- proposed a new sparse signal estimation for robotic imaging organizing forms. *e data monitored by the technology systems. Among them, noise and signal parameter node are passed adjacently on other nodes. During Journal of Robotics 3 Sink node Node Communications network Communication link Detection area management node Data management Figure 1: Multimedia enhancement technology framework. transmission, the image data are simultaneously enhanced it is very important to introduce the image processing by multiple nodes and then returned to the summary point variational model of the network-controlled robot image (sink node) through multiple hops. After processing, they transmission and processing system to deal with the reduced transmission and processing of image data under the are ﬁnally transmitted to the management node . *e multimedia enhancement technology mainly uses man- background that the nodes transmit information and re- agement nodes to enhance and control image information. sources cannot be used. Image information processing plays a very important role in multimedia enhancement technology. However, with 2.2. Variational Model of Image Processing. *e network the increasing amount and variety of image data, its func- remote control robot is diﬀerent from the traditional robot; tions face huge challenges. it refers to the robot that can realize the remote operation (1) *ere are eﬀects of energy consumption and com- under the network control. Its operating subject is generally munication delays. Multimedia enhancement tech- a professional. In the control environment, professionals use niques need to take into account the excess energy position sensors, visual feedback, and other means to achieve consumed by excess runtime. *erefore, people have remote network control of the system. *e research on developed a method of hibernation. However, even remote control has been in the exploratory stage since the with this method, there is no guarantee that the beginning of the 1980s. It has ﬂourished with the rise of consumption is reduced. computer network technology . With the development of computer network, remote control is no longer restricted (2) *ere is no ﬁxed time for information transmission by geographical conditions. *e emergence of the com- of multimedia enhancement technology. Without a munication network reduces the communication cost, ﬁxed time requirement, multiple kinds of data are thereby improving the high cost problem caused by the transmitted at the same time, which makes it diﬃcult design of dedicated connection lines in the traditional for the information receiver to process so much technology. Under the requirements of the development information at the ﬁrst time. environment of the times, network-controlled robots have (3) It is impossible to handle the performance standards become an important direction in current robotics research. at the same time. *e main diﬃculty is the confusion *e image transmission and processing system is the key of the dormancy mechanism, which makes the in- system for the network control robot to realize the image formation transmission not timely. Nowadays, task processing function. Generally, a camera equipped with multimedia enhancement technology only considers a computer is used to take pictures of the outside scene. In the optimization of one performance index, while the initial stage of the transmission of image data, the circuit ignoring the optimization of other performance is switched by the control signal. *e diﬀerential signal is indicators at the same time. converted into a digital image signal in a standard format *e multimedia enhancement technology manages the and then sent to the control unit for execution and pro- relevant image information and data due to those competing cessing. When the control unit realizes the processing function and the synchronization signal transmission nodes. Once it fails, it is diﬃcult for other applications in the entire framework to continue to provide services. *erefore, function at the same time, the image data that has been 4 Journal of Robotics initially processed is transmitted to the system memory for In this case, the additive noise ε satisﬁes 2 T buﬀering, so as to prepare for the needs of subsequent work. ε ∼ N(0, σ , D D ), resulting in k k k By waiting for the system command to be sent, the image ∗ 2 T (3) p v |v ∼ Nv ,σ , D D . data are sent to the converter together with the control signal k k k k k for conversion and converted into a speciﬁed analog T T T ∗ ∗ ∗ ∗ ∗ Let t � (v , −v ) � (t , t ) and t � (v , −v ) � 2 1 1 2 2 1 quantity according to the command requirements. Finally, (t , t ) ; then, there is 1 2 after the analog quantity is encoded and data are com- ∗ ∗ ∗ 2 T pressed, they are output through microwave. *e hardware p t |t ∼ p t |t , p t |t ∼ NHt ,σ , D D . k k k k k 3−k 3−k structure of the system is shown in Figure 2. *e exterior image captured by the camera is disturbed (4) by noise in the process of formation and transmission. After It is assumed that the tangent vector ﬁeld t of the image is the image data are transmitted in real time, in order to ensure the image quality during subsequent processing, the piecewise smooth, so that the prior information of the total variation is adopted, namely, system needs to perform basic processing through the image processing variational model in the control module. − α p(t|α)∝ e Ω|∇t|dx. *erefore, the image processing variational model mainly (5) completes the overall control of the image, and the signal ﬂowchart is shown in Figure 3. Among them: *e image processing of multimedia enhancement 2 2 2 2 2 (6) |∇t| � D t + D t + D t + D t . technology usually relies on software to complete. However, 1 1 2 1 1 2 2 2 with the continuous development of computer technology, *e estimation of t uses the maximum a posteriori es- real-time processing and transmission of image data based timation method, that is, maximization p(t|t ). It can be on algorithmic models is a new research direction . *e ∗ ∗ ∗ seen from the formula that p(t|t ) � p(t|t )p(t)/p(t ). model is based on a network-controlled robot image *erefore, maximizing p(t|t ) is equivalent to minimizing transmission and processing system, which realizes real- the log-likelihood function −log p(t|t ), as shown in time processing of image data, and it has good feasibility. Figure 4. *e image processing variational model is applied in the *e variational problem can be obtained by further multimedia enhancement technology, which can greatly derivation : shorten the operation time of image processing, and it has a very good enhancement eﬀect. η 2 T ∗ min Ω|∇t|dx + ΩD Ht − t dx. k (7) 3−k k *erefore, this paper uses the image processing varia- ∇·t�0 tional model to solve the image processing problem under the multimedia enhancement technology. *e variational Among them, incompatibility condition ∇ · t � 0 is used, model consists of two steps: estimating the tangent vector and η � σ /α. ﬁeld of the image and reconstructing the original image from It is noted that the diﬀerence operator appears in the the estimated tangent vector ﬁeld and the blurred image. *e second term of formula (7). *erefore, formula (7) itself is in application of variational model in multimedia enhance- the form of a variational model, which is more reasonable for ment technology is expounded from the discrete point of the estimation of tangent vector ﬁelds. However, due to the view. existence of the ﬁrst-order diﬀerence operator D , the so- First, the estimation of the tangent vector ﬁeld in lution of the variational problem is not unique, and the 2 T multimedia enhancement techniques needs to be consid- solutions diﬀer by a constant. Since ε ∼ N(0, σ , D D ), k k k ered. *e image collected in the image transmission and there is E(v ) � Hv according to the formula. *erefore, processing system of the network-controlled robot is additional constraints can be added to the tangent vector regarded as a two-dimensional surface deﬁned on the area ﬁeld t, resulting in the following minimization problem: Ω � [1, N ] × [1, N ]. *e parameter settings are shown in 1 2 η 2 T ∗ Table 1. min Ω|∇t|dx + ΩD Ht − t dx. 3−k k k (8) t∈U 2 Among them, the normal vector and tangent vector of k the image are denoted as n � ∇u � (D u, D u) and 1 2 T Among them, the function space t � ∇ u � (D u − D u) . *e diﬀerence operator is applied 2 1 U � t|∇ · t � 0, ΩHt � Ωt . Unlike formula (7), it can on both sides of the formula to obtain  be proved that when H is injective, the solution to the variational problem is unique, so formula (7) is used to D u � D Hu + D ε, k � 1,2, (1) k k k estimate the tangent vector ﬁeld t. *e distance of l between the unit image gradient and where H is the block circulant matrix corresponding to h. the estimated unit normal vector ﬁeld is used as the regu- ∗ ∗ Let D u � v D u � v , D ε � ε , and the operator is k k k k k larization term, resulting in the minimization problem of commutative in multimedia enhancement technology, so image restoration : v � Hv + ε . (2) k k k Journal of Robotics 5 image data image data image data Control Storage module module control Camera Interface conversion signal Encoding and Compression Module Microwave antenna Figure 2: Hardware structure of image transmission and processing system. Storage Encoding and Compression module Module image data image data image data clock signal clock signal clock signal Image Processing Start collecting signals Variational Models sync signal sync signal Figure 3: Signal ﬂowchart. Table 1: Parameter setting and its interpretation. Sequence Parameter setting Meaning 1 D First-order diﬀerence operator in the horizontal direction 2 D First-order diﬀerence operator in the vertical direction 3 n *e normal vector of the image 4 t Tangent vector of the image 0.0020 ∇u n λ (9) min Ω − dx + Ω