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This study reports serial swallowing functional changes in a patient with an acute ischemic stroke but normal diffusion weighted imaging (DWI) scans. A 71-year-old man presented with dysphagia. Laryngoscopy revealed left arytenoid swelling and hypomobility of the left vocal cord. Acute lesions were not observed on brain magnetic resonance imaging. On the 9th day following hospital admission, the first videofluoroscopic swallowing study (VFSS) was performed. When he swallowed semi-solid food, significant pre- and post-swallowing aspirations were detected without coughing. An acute stroke with sudden-onset dysphagia was considered as the possible cause of dysphagia. The serial VFSSs showed gradual improvement in the swallowing reflex and persistent moderate cricopharyngeal dysfunction. During the VFSS anteroposterior view, bolus swallowing was tolerated, and aspiration signs disappeared, as the head was turned to the left and tilted to the right, indicating the role of a lateralizing lesion in symptom onset. This case study reports the course of natural resolution of dysphagia in a patient with a DWI-negative stroke based on serial VFSS results. There is a need to establish the significance of intensive dysphagia rehabilitation, including neuromuscular electrical stimulation therapy, in patients clinically diagnosed with an acute stroke. Therefore, further studies involving a larger population of patients with DWI-negative strokes and dysphagia are warranted.

During the last decade, the security of digital images has received considerable attention in various multimedia transmission schemes. However, many current cryptosystems tend to adopt a single-layer permutation or diffusion algorithm, resulting in inadequate security. A hierarchical bilateral diffusion architecture for color image encryption is proposed in response to this issue, based on a hyperchaotic system and DNA sequence operation. Primarily, two hyperchaotic systems are adopted and combined with cipher matrixes generation algorithm to overcome exhaustive attacks. Further, the proposed architecture involves designing pixelpermutation, pixel-diffusion, and DNA (deoxyribonucleic acid) based block-diffusion algorithm, considering system security and transmission efficiency. The pixel-permutation aims to reduce the correlation of adjacent pixels and provide excellent initial conditions for subsequent diffusion procedures, while the diffusion architecture confuses the image matrix in a bilateral direction with ultra-low power consumption. The proposed system achieves preferable number of pixel change rate (NPCR) and unified average changing intensity (UACI) of 99.61% and 33.46%, and a lower encryption time of 3.30 seconds, which performs better than some current image encryption algorithms. The simulated results and security analysis demonstrate that the proposed mechanism can resist various potential attacks with comparatively low computational time consumption.

To solve the edge ringing or block effect caused by the partial differential diffusion in image enhancement domain, a new image enhancement algorithm based on bidirectional diffusion, which smooths the flat region or isolated noise region and sharpens the edge region in different types of defect images on aviation composites, is presented. Taking the image pixel's neighborhood intensity and spatial characteristics as the attribute descriptor, the presented bidirectional diffusion model adaptively chooses different diffusion criteria in different defect image regions, which are elaborated are as follows. The forward diffusion is adopted to denoise along the pixel's gradient direction and edge direction in the pixel's smoothing area while the backward diffusion is used to sharpen along the pixel's gradient direction and the forward diffusion is used to smooth along the pixel's edge direction in the pixel's edge region. The comparison experiments were implemented in the delamination, inclusion, channel, shrinkage, blowhole and crack defect images, and the comparison results indicate that our algorithm not only preserves the image feature better but also improves the image contrast more obviously.

We introduce LDSeCom, a novel lightweight-to-diffusion framework for semantic image communication. LDSeCom addresses bandwidth constraints by developing LSNet, a lightweight, loop-based segmentation model at the sender, and an improved diffusion model guided by our AFM-Net at the receiver. LSNet efficiently compresses images into semantic maps, while AFM-Net’s adaptive feature modulation ensures high-quality image reconstruction. On benchmark datasets, our LSNet achieves competitive accuracy with only 0.5M parameters, while our diffusion model improves image reconstruction quality by up to 28.51% mFID. The framework enables high-fidelity results from semantic maps compressed to??of the original size, proving its efficiency for bandwidth-constrained scenarios.

The ASV-SR method introduces an innovative approach to single-image super-resolution (SISR) by integrating adaptive Stochastic Variation within a diffusion model. This combination effectively captures pixel interactions and various patterns, addressing long-range dependencies in images and overcoming the limitations of traditional deterministic SISR methods. Extensive evaluations on diverse image datasets, including PSNR, SSIM, and LPIPS metrics, reveal that the proposed model outperforms current state-of-the-art techniques. Additionally, the incorporation of a modified SWIN transformer (MST) enhances feature extraction, improving the model's adaptability and efficiency in tackling SISR challenges. This comprehensive approach underscores the significance of incorporating stochastic processes like stochastic variation to advance image super-resolution.

We report the clinical usefulness of elucidating the state of the corticospinal tract (CST) by the use of diffusion tensor image tractography (DTT) in hemiparetic stroke patients. DTT was performed using 1.5 T magnetic resonance imaging. DTT demonstrated that the CST of the affected hemisphere was preserved in the medial portion of the hematoma in patient 1, but was interrupted by a hematoma in patient 2. DTT seems to be useful for elucidating the status of the CST in hemiparetic stroke patients.

The purpose of this study would evaluate if having clinical effects on diffusion image with quantitative analysis through ADC values of brain’s normal tissue and lesions before and after contrast injections using a 3.0T. From November in 2007 until December in 2008, a total of 32 patient was performed on 3.0T(Signa Excite, GE Medical System, USA) with the normal or lesions in the patient who requests diffusion weighted image with 8channel head coil. The pulse sequence was used with spin echo EPI(TR: 10000msec, TE: 72.2 msec, Matrix: 128*128, FOV: 240 mm, NEX: 1, diffusion direction: 3, b-value: 1000). Measurement results of ADC values on lesions, CSF, white matter, gray matter, lesions after contrast injection were measured less 75% than before contrast injection, infarction: 100%, CSF: 78%(high), white matter: 71.4%(low), gray matter: 50%(high, low). The results of paired t-test on the deference of ADC values which statically is significant in three(lesions, CSF, white matter)regions except for white matter(p<0.05). Quantitative analysis of lesions, CSF, white matter, gray matter have difference on all regions. ADC values were low in lesions and white matter, normal CSF after contrast injection commonly is high than before contrast injection, ADC values which white matter were high and low (50:50) after contrast injection. 3.0T diffusion weighted image clinically supposed that performing DWI examination after contrast injection was not desirable because of having effects on brain tissue.

자전거 이용 증가와 함께 자전거도로의 사고 예방 및 위험 상황에 대한 영상 기반 모니터링 의 중요성이 더욱 부각되고 있다. 그러나 계절·조도·기상 변화가 충분히 반영된 영상 데이터를 확보하는 데에는 많은 시간이 소요되고, 설상 가상으로 라벨링 비용 또한 높아 객체 탐지 모델 개발에 제약이 발생한다. 본 연구는 이러한 문제를 해결하기 위해 구조적 제약과 스타일 적응 을 동시에 반영하는 영상 합성 기법을 제안하였다. 제안한 방법은 Stable Diffusion 기반 모델에 ControlNet과 Low-Rank Adaptation을 결합하여, 마스크 영상을 통한 구조 제어와 스타일 미세조 정을 통합적으로 수행한다. 실제 CCTV 영상을 기반으로 데이터세트를 구축하고, 세 가지 Stable Diffusion 계열 기저 모델을 대상으로 합성 성능을 비교하였다. 성능 평가는 Fréchet Inception Distance와 CLIP-score를 활용하였으며, 그 결과 제안한 방법이 사실성과 텍스트 정합 성 측면에서 우수한 합성 품질을 달성함을 확인하였다. 또한 텍스트 프롬프트 조작만으로 계 절 및 기상 조건을 반영한 영상 생성이 가능함을 검증하였다. 본 연구는 촬영이 어려운 다양한 환경 조건의 데이터를 효율적으로 생성할 수 있어 자전거도로 모니터링을 위한 데이터 부족 문제 해결에 기여하며, 향후 객체 탐지 및 안전관리 기술의 고도화에 효과적으로 활용될 수 있다.

The growing use of bicycles has heightened the importance of video-based monitoring for preventing accidents and detecting hazardous situations on bicycle roads. However, collecting video data that adequately reflects variations in season, illumination, and weather requires substantial time, and the high cost of data labeling further limits the development of effective object-detection models. To address these challenges, this study proposes an image synthesis method that simultaneously incorporates structural constraints and style adaptation. The proposed approach integrates Stable Diffusion with ControlNet and Low-Rank Adaptation (LoRA), enabling unified control of scene structure through mask images and fine-grained style adjustment. A dataset was constructed using real CCTV footage, and three Stable Diffusion–based backbone models were evaluated for their synthesis performance. Fréchet Inception Distance and CLIP-score were used for quantitative assessment, demonstrating that the proposed method achieves superior realism and semantic alignment between images and text. Furthermore, the model successfully generated images reflecting seasonal and weather variations solely through prompt manipulation. This research provides an efficient solution for generating diverse environmental conditions that are difficult to capture in practice, thereby alleviating data scarcity in bicycle-road monitoring and supporting the advancement of nextgeneration object-detection and safety-management technologies.

대장암은 전 세계적으로 인간 건강에 심각한 위협을 가하는 주요 질병 중 하나이며, 이에 대한 조기 진단과 치료는 생존율을 크게 향상할 수 있다. 최근 인공지능(AI) 기술의 발전으로 대장암 진단 및 치 료 접근 방식에 혁신적인 변화가 이루어지고 있으나, 인공지능 모델의 학습을 위해서는 대량의 고품질 의료 데이터가 필수적이다. 그러나 의료 데이터의 획득은 프라이버시, 저작권, 그리고 데이터 공유에 대한 엄격한 정책으로 인해 상당한 도전 과제를 안고 있다. 본 연구는 Semantic Map을 조건으로 하 는 SDM(Semantic Diffusion Model)을 활용하여 실제와 유사한 고품질의 대장 내시경 이미지를 생성 하는 데 성공하였다. 이 과정을 심층적으로 분석하여, 의료 데이터의 접근성 문제에 대한 혁신적인 해 결책을 제시하고자 한다.

차세대 ICT 환경의 변화에 의한 커뮤니케이션의 변 화, 특히 개방혁신 플랫폼의 변화는 어떻게 펼쳐질 것인가? 전화를 대신하는 카카오톡, 가족과 친구의 개념을 변화시킨 페이스 북, 그리고 기업프로모션의 지형을 바꾸고 있는 트위터를 경험하면 서, 우리는 멀티미디어 기반 커뮤니케이션이 미래의 기업과 시장활동에 미칠 영향력에 대해서 궁금증과 기대를 품고 있다. 특히 이미지/영상 기반 커뮤니케이션이 일상화 되면서, 개방 혁신 플랫폼의 확산과 참여자의 증가, 그리고 crowd computing(크라우드 컴퓨 팅, 혹은 대중 컴퓨팅)의 일반화가 가져오고 있는 디지털 재화의 개 념, 유통, 소비의 변화는 디지털 혁신의 고유한 확산성에 대해서 다시 한번 주목하게 하고 있다. 기업이 생산하 고, 소비자가 소비하는 전통적 시장의 개념은 디지털 경제의 네트워크에 의해 생 산, 유통, 소비가 일원화하고 있는 미래의 디지털 시장에서의 소비자와 참여적 생산 자, 그리고 그들간의 가치와 교환의 원리를 충분히 설명할 수 없게 되었 다. 우리는 이와 같은 미래 디지털 경제의 새로운 지평에 대해서 예측하고 설명할 수 있는 이론적 연구와 효과적인 연구틀을 개발하기 위하여 다음과 같은 단계를 통하여 연구를 진행하였 다. 첫 째, 현재 이미지 기반 커뮤니케이션을 사용하는 사용자와 커뮤니 티, 그리고 대화의 형식에 대해서 관찰하였 다. 그들은 다양한 SNS를 이용하 고, SNS의 아이덴티티와 사회망을 연결하는 이미지/영상 서비스들을 통해 자신의 정보를 제공하거나 사회망 내의 멀티미디어 정보들을 수 집, 교 환, 제고하는 네트워크 활동에 의해서 사회망 교류 의 활동을 유지하고 성장시켜나가고 있음이 관찰되었다. 둘째, 우리는 이상의 관찰을 기반으로 개방혁신 플랫폼의 요건사항을 정리하 고, 시뮬레이션 실험 그룹 참가자들에 의한 플랫폼 테스트를 진행하였다. 참가자들은 이미지 기반 SNS를 이용하여 미래 디지털 기기 혹은 서비스를 디자인하는 과제를 16주에 걸쳐서 진행하였다. 셋 째, 이상에서 수집된 모의 실험의 양적, 질적 데이터를 기반으로 미래 참여적 개방혁신 플랫폼의 모형화를 진행하였다. 이 때 모형화는 주로 선물경제(Gift Economy)에 의한 게임화(Gamifiaction) 과정에서 관찰된 동기(motivation), 관여(engagement), 보상(rewards)의 기제화에 의한 참여의 특성과 변화에 대해서 기술하고, 이로 인한 확산효과의 구조에 대해서 이론화 하였 다

경태람은 중국의 무형문화유산으로서 풍부한 역사적·문화적 함의와 독창적인 예술적 가치를 지니고 있다. 그러나 현대화의 급속한 진전에 따라 전통 공예의 단절 현상이 심화되고 있으며, 경태람 예술 역시 전승과 혁신이라는 이중적 과제에 직면하고 있다. 본 연구는 Stable Diffusion과 Midjourney 기술을 중심으로 생성형 인공지능(AI)을 활용하여 시각 이미지 생성의 혁신적인 방법을 탐구하고, 경태람의 문화상품 이미지를 설계하고 생성하는 데 목적을 두었다. 문헌 연구, 디자인 실험과 전문가 평가의 방법론을 적용하였으며, 연구 범위는 생성형 AI 도구, 무형문화유산과 문화상품의 개념, 무형문화유산 디지털화의 새로운 경로 탐색 및 디자인 실천을 포함한다. 실험 단계에서는 경태람 관련 지식 그래프를 구축하여 이론적 토대를 마련하고, LoRA 모델을 구성하고 학습시킨 후, 이를 기반으로 Stable Diffusion을 제어하여 경태람 문화상품의 시각 이미지를 생성하였다. 생성된 이미지는 Midjourney를 활용하여 최적화하였다. 실험 결과, Stable Diffusion과 Midjourney는 창의성과 예술성을 갖춘 문화상품 시각 이미지를 효과적으로 생성하였으며, 이는 경태람에 새로운 시대적 활력과 창조적 가능성을 부여하고, 무형문화유산의 디지털 전승 및 혁신에 기여할 수 있음을 입증하였다.

As an intangible cultural heritage of China, Cloisonne embodies rich historical and cultural significance alongside unique artistic value. However, rapid modernization has led to a growing disconnection in traditional crafts, posing challenges for both preservation and innovation. This study explores innovative visual image generation methods using generative AI technologies—specifically Stable Diffusion and Midjourney—to design and create cultural product images inspired by Cloisonne. Employing literature review, design practice and expert evaluation, the research covers generative AI tools, concepts of intangible cultural heritage and cultural goods, and new approaches to digitalization and design practice. Experimentally, a Cloisonne knowledge graph was constructed as a theoretical basis, followed by building and training a LoRA model. Stable Diffusion was then used to generate visual images of Cloisonne cultural products, which were further refined with Midjourney. Results demonstrate that these AI tools effectively produce creative and artistic cultural product visuals, revitalizing Cloisonne with fresh vitality and innovation, while advancing the digital preservation and modernization of intangible cultural heritage.

Because the single chaotic mapping easily creates security weaknesses in the image encryption algorithm, the security needs to be improved. Aiming at this problem, a bit-level image encryption algorithm based on composite chaotic mapping (CCM-IEA) is proposed. First of all, the algorithm scrambles the plain image on bit level through the Cat mapping for the first time. And then the Henon mapping of two dimensional discrete is used to scramble for the second time. Finally, image diffusion is operated through the one dimensional Logistic mapping, and the data sensitivity is enhanced. The experimental results show that the performance of the CCM-IEA algorithm is better on the histogram, information entropy and correlation analysis. Compared with the single chaotic images encryption algorithm, the CCM-IEA algorithm has the ability to resist the information entropy analysis and correlation analysis. It can be seen that the CCM-IEA algorithm has high safety performance and good encryption effect.

In this paper, on the basis of the anisotropic diffusion mechanism, analyzes emphatically represented by P - M model of diffusion filter principle of several kinds of nonlinear diffusion model, and their respective characteristics and problems. In-depth analysis of the nonlinear diffusion model, the threshold and termination mechanism of combining image geometric structure feature and visual information (gradient, brightness, contrast, structural information), in view of the existing nonlinear diffusion filtering model, the diffusion coefficient depends on the gradient and the problem that the susceptible to noise interference, presents a fidelity term used in image denoising and restoration contain nonlinear wavelet diffusion model, the theoretical analysis and experimental results show that this method is compared with other diffusion model while denoising can keep image edges and details characteristics, image visual effect is better.

In this paper, on the basis of the anisotropic diffusion mechanism, analyzes emphatically represented by P - M model of diffusion filter principle of several kinds of nonlinear diffusion model, and their respective characteristics and problems. In-depth analysis of the nonlinear diffusion model, the threshold and termination mechanism of combining image geometric structure feature and visual information (gradient, brightness, contrast, structural information), in view of the existing nonlinear diffusion filtering model, the diffusion coefficient depends on the gradient and the problem that the susceptible to noise interference, presents a fidelity term used in image denoising and restoration contain nonlinear wavelet diffusion model, the theoretical analysis and experimental results show that this method is compared with other diffusion model while denoising can keep image edges and details characteristics, image visual effect is better.

Image compression-decompression methods have become increasingly crucial in modern times, facilitating the transfer of high-quality images while minimizing file size and internet traffic. Historically, early image compression relied on rudimentary codecs, aiming to compress and decompress data with minimal loss of image quality. Recently, a novel compression framework leveraging colorization techniques has emerged. These methods, originally developed for infusing grayscale images with color, have found application in image compression, leading to colorization-based coding. Within this framework, the encoder plays a crucial role in automatically extracting representative pixels—referred to as color seeds—and transmitting them to the decoder. The decoder, utilizing colorization methods, reconstructs color information for the remaining pixels based on the transmitted data. In this paper, we propose a novel approach to image compression, wherein we decompose the compression task into grayscale image compression and colorization tasks. Unlike conventional colorization-based coding, our method focuses on the colorization process rather than the extraction of color seeds. Moreover, we employ the Denoising Diffusion Null-Space Model (DDNM) for colorization, ensuring high-quality color restoration and contributing to superior compression rates. Experimental results demonstrate that our method achieves higher-quality decompressed images compared to standard JPEG and JPEG2000 compression schemes, particularly in high compression rate scenarios.

In this paper, a novel image encryption method which is based on the coupled map lattice (CML) and two-stage diffusion is proposed. The author employs the two-stage diffusion to process images. The plain image is expanded into two composed of selected four bit-planes and diffuse them at bit-level as first stage diffusion, then reconstruct them as the input of block diffusion, which is served as second stage diffusion. The chaotic coupled map lattice employed in this method generates pseudo-random sequences in block diffusion phase. The experiment results and analysis have proved the novel image encryption method is practical and effective for encryption applications.

GGAC is an improvement based on geodesic active contour model (GAC). GGAC model is a widely used method for image segmentation. But, it will be difficult to achieve satisfactory segmentation results to the texture, uneven structure, edge particles, weak edge and other features of the wood surface image. Therefore, the author proposes a segmentation method that integrates the improved Canny edge detection result integrated into the improved GGAC model redrawing boundary stop function, and uses the improved variational level set method to achieve the numerical solution. The algorithm has reduced the choice sensitivity to the initial contour and enhanced the scalability, which can make the profile curve converge to defect edges more rapidly, avoid the local optimum, and improve segmentation effects of weak edges and uneven image. The results are clearer, more consistent and real-time. It has provided a more effective way to segment the wood surface defects, and broadened the application scope of Canny operator and an improved geodesic active contour model.