Image Stitching Technique Research Articles

Enhancing Situational Awareness with VAS-Compass Net for the Recognition of Directional Vehicle Alert Sounds

People with hearing impairments often face increased risks related to traffic accidents due to their reduced ability to perceive surrounding sounds. Given the cost and usage limitations of traditional hearing aids and cochlear implants, this study aims to develop a sound alert assistance system (SAAS) to enhance situational awareness and improve travel safety for people with hearing impairments. We proposed the VAS-Compass Net (Vehicle Alert Sound–Compass Net), which integrates three lightweight convolutional neural networks: EfficientNet-lite0, MobileNetV3-Small, and GhostNet. Through employing a fuzzy ranking ensemble technique, our proposed model can identify different categories of vehicle alert sounds and directions of sound sources on an edge computing device. The experimental dataset consisted of images derived from the sounds of approaching police cars, ambulances, fire trucks, and car horns from various directions. The audio signals were converted into spectrogram images and Mel-frequency cepstral coefficient images, and they were fused into a complete image using image stitching techniques. We successfully deployed our proposed model on a Raspberry Pi 5 microcomputer, paired with a customized smartwatch to realize an SAAS. Our experimental results demonstrated that VAS-Compass Net achieved an accuracy of 84.38% based on server-based computing and an accuracy of 83.01% based on edge computing. Our proposed SAAS has the potential to significantly enhance the situational awareness, alertness, and safety of people with hearing impairments on the road.

Advancing Electric Engineering Education through Immersive Virtual Reality: Deep Learning and Evolutionary Algorithms for Image Stitching and Rectification in Virtual Lab Environments

Virtual Reality (VR) technology has emerged as a transformative tool in education, offering immersive and interactive experiences that enhance learning outcomes. This paper delves into the application of image stitching and rectification techniques to create a VR lab environment, specifically tailored for electrical engineering education. The importance of VR technology in education is explored, highlighting its role in promoting active learning and providing experiential learning opportunities. The primary emphasis of this Paper lies in the smooth incorporation of image stitching algorithms for the creation of panoramic perspectives, along with the implementation of rectification techniques to correct irregular borders within the stitched images. By utilizing Convolutional Neural Networks (CNNs) and Genetic Algorithms (GAs), the proposed approach optimizes the rectification process, resulting in visually cohesive representations. Demonstrating the utilization of the VR lab across a range of situations, such as examining power transfer and creating control panels for water pumps in irrigation initiatives, the immersive setting enables students to delve into intricate systems. The performance of the proposed method was evaluated using various metrics, including mean squared error, peak signal to noise ratio (PSNR), structural similarity index (SSIM), and Fréchet inception distance (FID). the combination of deep learning algorithm specifically (CNN) and optimization algorithm specifically (Genetic algorithm (GA)) led to an increase in the accuracy of the rectified images where the average PSNR reached 23.98, SSIM was 0.8066, and FID was 18.72. Regarding the users’ opinion about the generated environment by stitching and rectifying images, participants demonstrated consistent positive sentiments, with mean scores ranging from 3.65 to 4.03, all above the scale midpoint, and moderate variability indicated by standard deviation values ranging from 1.070 to 1.251, suggesting general favorability with some variation in responses. This experience empowers the users to gain insights and cultivate essential problemsolving abilities at a heightened level. Collaborative learning is facilitated, enabling students to engage in collaborative projects regardless of their physical location. Through the synthesis of image processing techniques and VR technology, this research contributes to the enrichment of educational experiences and the advancement of electrical engineering education.

Harnessing multimodal data fusion to advance accurate identification of fish feeding intensity

Accurately identifying the fish feeding intensity plays a vital role in aquaculture. While traditional methods are limited by single modality (e.g., water quality, vision, audio), they often lack comprehensive representation, leading to low identification accuracy. In contrast, the multimodal fusion methods leverage the fusion of features from different modalities to obtain richer target features, thereby significantly enhancing the performance of fish feeding intensity assessment (FFIA). In this work a multimodal dataset called MRS-FFIA was introduced. The MRS-FFIA dataset consists of 7611 labelled audio, video and acoustic dataset, and divided the dataset into four different feeding intensity (strong, medium, weak, and none). To address the limitations of single modality methods, a Multimodal Fusion of Fish Feeding Intensity fusion (MFFFI) model was proposed. The MFFFI model is first extracting deep features from three modal data audio (Mel), video (RGB), Acoustic (SI). Then, image stitching techniques are employed to fuse these extracted features. Finally, the fused features are passed through a classifier to obtain the results. The test results show that the accuracy of the fused multimodal information is 99.26%, which improves the accuracy by 12.80%, 13.77%, and 2.86%, respectively, compared to the best results for single-modality (audio, video and acoustic dataset). This result demonstrates that the method proposed in this paper is better at classifying the feeding intensity of fish and can achieve higher accuracy. In addition, compared with the mainstream single-modality approach, the model improves 1.5%–10.8% in accuracy, and the lightweight effect is more obvious. Based on the multimodal fusion method, the feeding decision can be optimised effectively, which provides technical support for the development of intelligent feeding systems.

Advanced Image Stitching Method for Dual-Sensor Inspection.

Efficient image stitching plays a vital role in the Non-Destructive Evaluation (NDE) of infrastructures. An essential challenge in the NDE of infrastructures is precisely visualizing defects within large structures. The existing literature predominantly relies on high-resolution close-distance images to detect surface or subsurface defects. While the automatic detection of all defect types represents a significant advancement, understanding the location and continuity of defects is imperative. It is worth noting that some defects may be too small to capture from a considerable distance. Consequently, multiple image sequences are captured and processed using image stitching techniques. Additionally, visible and infrared data fusion strategies prove essential for acquiring comprehensive information to detect defects across vast structures. Hence, there is a need for an effective image stitching method appropriate for infrared and visible images of structures and industrial assets, facilitating enhanced visualization and automated inspection for structural maintenance. This paper proposes an advanced image stitching method appropriate for dual-sensor inspections. The proposed image stitching technique employs self-supervised feature detection to enhance the quality and quantity of feature detection. Subsequently, a graph neural network is employed for robust feature matching. Ultimately, the proposed method results in image stitching that effectively eliminates perspective distortion in both infrared and visible images, a prerequisite for subsequent multi-modal fusion strategies. Our results substantially enhance the visualization capabilities for infrastructure inspection. Comparative analysis with popular state-of-the-art methods confirms the effectiveness of the proposed approach.

Internal thread defect detection system based on multi-vision.

In the realm of industrial inspection, the precise assessment of internal thread quality is crucial for ensuring mechanical integrity and safety. However, challenges such as limited internal space, inadequate lighting, and complex geometry significantly hinder high-precision inspection. In this study, we propose an innovative automated internal thread detection scheme based on machine vision, aimed at addressing the time-consuming and inefficient issues of traditional manual inspection methods. Compared with other existing technologies, this research significantly improves the speed of internal thread image acquisition through the optimization of lighting and image capturing devices. To effectively tackle the challenge of image stitching for complex thread textures, an internal thread image stitching technique based on a cylindrical model is proposed, generating a full-view thread image. The use of the YOLOv8 model for precise defect localization in threads enhances the accuracy and efficiency of detection. This system provides an efficient and intuitive artificial intelligence solution for detecting surface defects on geometric bodies in confined spaces.

Lateral response artifact correction method using image stitching technique in radiochromic film dosimetry.

Lateral response artifact (LRA) is caused by the interaction between film and flatbed scanner in the direction perpendicular to the scanning direction. This can significantly affect the accuracy of patient-specific quality assurance (QA) in cases involving large irradiation fields. We hypothesized that by utilizing the central area of the flatbed scanner, where the magnitude of LRA is relatively small, the LRA could be mitigated effectively. This study proposes a practical solution using the image-stitching technique to correct LRA for patient-specific QA involving large irradiation fields. Gafchromic™ EBT4 film and Epson Expression ES-G11000 flatbed scanner were used in this study. The image-stitching algorithm requires a spot between adjacent images to combine them. The film was scanned at three locations on a flatbed scanner, and these images were combined using the image-stitching technique. The combined film dose was then calculated and compared with the treatment planning system (TPS)-calculated dose using gamma analysis (3%/2mm). Our proposed LRA correction was applied to several films exposed to 18 × 18 cm2 open fields at doses of 200, 400, and 600 cGy, as well as to four clinical Volumetric Modulated Arc Therapy (VMAT) treatment plans involving large fields. For doses of 200, 400, and 600 cGy, the gamma analysis values with and without LRA corrections were 95.7%versus 67.8%, 95.5%versus 66.2%, and 91.8%versus 35.9%, respectively. For the clinical VMAT treatment plan, the average pass rate±standard deviation in gamma analysis was 94.1%±0.4% with LRA corrections and 72.5%±1.5% without LRA corrections. The effectiveness of our proposed LRA correction using the image-stitching technique was demonstrated to significantly improve the accuracy of patient-specific QA for VMAT treatment plans involving large irradiation fields.

Designing a Virtual Campus Tour using Image Stitching Techniques to Provide Information on College Entrance Test

The University of Bengkulu administers college entrance exams, however some test takers still require assistance in locating the correct room, despite the building being marked. It is crucial to avoid errors in finding the right test room, as it can cause potential students to waste valuable time. Therefore, a more precise and practical solution is necessary to provide information on test locations. This study designs a location-based virtual tour that offers a 360-degree view, providing information on the location of each building and the conditions inside and outside each test room. The virtual tour encompasses 81 buildings, including test rooms, with 28 to 32 images captured at each location, then stitched together using image stitching techniques. The goal of the virtual tour is to create a comprehensive view of the test location and provide more detailed information on the room's condition. Furthermore, the usability of this virtual tour was tested on 140 high school students as potential test participants, utilizing the System Usability Scale (SUS) to evaluate its effectiveness, resulting in a score of 72.19. In other words, the virtual tour was found to be an effective tool in helping users understand the test location.

Rapid Detection and Analysis of Raman Spectra of Bacteria in Multiple Fields of View Based on Image Stitching Technique.

Due to antibiotic abuse, the problem of bacterial resistance is becoming increasingly serious, and rapid detection of bacterial resistance has become an urgent issue. Because under the action of antibiotics, different active bacteria have different metabolism of heavy water, antibiotic resistance of bacteria can be identified according to the existence of a C-D peak in the 2030-2400 cm-1 range in the Raman spectrum. To ensure data veracity, a large number of bacteria need to be detected, however, due to the limitation of the field of view of the high magnification objective, the number of single cells in a single field of view is very small. By combining an image stitching algorithm, image recognition algorithm, and processing of Raman spectrum and peak-seeking algorithm, can identify and locate single cells in multiple fields of view at one time and can discriminate whether they are Antimicrobial-resistant bacteria. In experiments 1 and 2, 2706 bacteria in 9 × 11 fields of view and 2048 bacteria in 11 × 11 fields of view were detected. Results showed that in experiment 1, there are 1137 antibiotic-resistant bacteria, accounting for 42%, and 1569 sensitive bacteria, accounting for 58%. In experiment 2, there are 1087 antibiotic-resistant bacteria, accounting for 53%, and 961 sensitive bacteria, accounting for 47%. It showed excellent performance in terms of speed and recognition accuracy as compared to traditional manual detection approaches. And solves the problems of low accuracy of data, a large number of manual experiments, and low efficiency due to the small number of single cells in the high magnification field of view and different peak-seeking parameters of different Raman spectra. The detection and analysis method of bacterial Raman spectra based on image stitching can be used for unattended, automatic, rapid and accurate detection of single cells at high magnification with multiple fields of view. With the characteristics of automatic, high-throughput, rapid, and accurate identification, it can be used as an unattended, universal and non-invasive means to measure antibiotic-resistant bacteria to screen for effective antibiotics, which is of great importance for studying the persistence and spread of antibiotics in bacterial pathogens.

Displacement Field Calculation of Large-Scale Structures Using Computer Vision with Physical Constraints: An Experimental Study

In recent years, computer vision-based structural displacement acquisition technique has received wide attention and research due to the advantages of easy deployment, low-cost, and non-contact. However, the displacement field acquisition of large-scale structures is a challenging topic as a result of the contradiction of camera field-of-view and resolution. This paper presents a large-scale structural displacement field calculation framework with integrated computer vision and physical constraints using only one camera. First, the full-field image of the large-scale structure is obtained by processing the multi-view image using image stitching technique; second, the full-field image is meshed and the node displacements are calculated using an improved template matching method; and finally, the non-node displacements are described using shape functions considering physical constraints. The developed framework was validated using a scaled bridge model and evaluated by the proposed evaluation index for displacement field calculation accuracy. This paper can provide an effective way to obtain displacement fields of large-scale structures efficiently and cost-effectively.

Image Stitching Techniques Applied to Plane or 3-D Models: A Review

Image stitching is a technique in which multiple overlapping images of the scene are stitched together to generate an image with wide view and high resolution. Image stitching methods can be broadly classified into feature-based methods and deep learning methods. Feature-based methods use manually designed features to establish transformation relationships between multiple images. This technology has played an important role in medical, industrial, military, and other fields. With the rise of deep learning in computer vision, it has also become the mainstream method in the field of image stitching. This paper provides a systematic literature review of image stitching techniques applied on the plane and 3D models for both feature-based and deep learning methods. We divide the stitching methods into two categories, namely mosaic stitching methods for generating stitched plane images and panoramic stitching methods for generating stitched panoramic images. Based on the camera type, it is further divided into pinhole camera plane stitching methods, pinhole camera panoramic stitching methods, fisheye camera panoramic stitching methods, and light field camera plane stitching methods. Extensive search was conducted in ICIP, TIP, ICCV, ECCV, CVPR, BMVC, ICPR, IJCV, AIM, ITS, TPAMI, SIGGRAFH to summarize related image stitching techniques. 89 articles are selected for systematic literature review and are presented in the table. The objective of this systematic literature review is to provide detailed technical progress in image stitching techniques and to identify the research gap in this field.

Implementasi Virtual Tour JTE UNRI Menggunakan Kombinasi Foto Panorama dan Rekayasa 3D

Media information has a very important role in efforts to attract prospective students to join. A website needs to be improved in the era of large technological developments. One of the new information media that utilizes the latest technology is a virtual tour. If you look at some of the previous ones, making a virtual tour can be made using panoramic photos made with image stitching techniques from many photos and can also use 3-dimensional engineering results. Both ways have their advantages and disadvantages. In this study, we will combine panoramic photos and 3D engineering to create a virtual tour of the Department of Electrical Engineering, Riau University. Virtual tour created using an equirectangular image base. There are 10 locations included in the virtual tour which four of them use panoramic photos and the remaining six places use 3D engineering. Four panoramic photos and six 3D-engineered equirectangular shapes are combined into one virtual tour. To validate the results of combining panoramic photos and 3D engineering into a virtual tour, user experience testing was conducted using a user experience questionnaire. User experience questionnaire was distributed to 30 respondents. As a result, four scales are in the excellent category: attractiveness, dependability, stimulation, and novelty. Then two scales are included in the good category, namely perspicuity and efficiency, which get a value of 1.78. From the user experience perspective, virtual tours resulting from a combination of panoramic photos and 3D engineering in general can be considered very good. Then this combination of panoramic photos and 3D engineering can be used as a solution for virtual tour documentation if several locations are inadequate to document

SIFT-CNN Pipeline in Livestock Management: A Drone Image Stitching Algorithm

Images taken by drones often must be preprocessed and stitched together due to the inherent noise, narrow imaging breadth, flying height, and angle of view. Conventional UAV feature-based image stitching techniques significantly rely on the quality of feature identification, made possible by image pixels, which frequently fail to stitch together images with few features or low resolution. Furthermore, later approaches were developed to eliminate the issues with conventional methods by using the deep learning-based stitching technique to collect the general attributes of remote sensing images before they were stitched. However, since the images have empty backgrounds classified as stitched points, it is challenging to distinguish livestock in a grazing area. Consequently, less information can be inferred from the surveillance data. This study provides a four-stage object-based image stitching technique that, before stitching, removes the background’s space and classifies images in the grazing field. In the first stage, the drone-based image sequence of the livestock on the grazing field is preprocessed. In the second stage, the images of the cattle on the grazing field are classified to eliminate the empty spaces or backgrounds. The third stage uses the improved SIFT to detect the feature points of the classified images to o8btain the feature point descriptor. Lastly, the stitching area is computed using the image projection transformation.

(Retracted) Mural digital image restoration technology and stitching evaluation model based on machine learning

Mural painting refers to art on the wall, which has high aesthetic value. However, due to its vulnerability to damage, digital image restoration and stitching techniques are now used to permanently preserve mural images. The purpose of our study is to select a more suitable specific calculation method by studying machine learning (ML) methods and conduct in-depth research on mural digital image restoration technology and stitching evaluation, so that it can better serve the restoration and splicing of the current mural digital images. Based on the experiments, it can be seen that the respondents in hall A had a high degree of recognition for digital image restoration and stitching technology. There were 176 respondents, and only 29 people believed that digital image restoration was not important; respondents in hall C did not agree with the digital image restoration technology as much as hall A, only 132 people thought it was important, and 64 people thought it was not important. It can be seen that the model establishment of mural digital image restoration technology and splicing evaluation is deeply affected by the knowledge level of the evaluator. The experimental results of our study showed that the research process of mural digital image restoration technology and stitching evaluation based on ML method was more effective than other methods of analyzing experimental data.

Systematic Approach for Alignment of Light Field Mirage

We previously proposed techniques to eliminate repeated three-dimensional (3D) images produced by the light field Mirage, which consists of circularly aligned multiple-slanted light field displays. However, we only constructed the lower half of the system to verify the proposed elimination techniques. In this study, we developed an alignment technique for a complete 360-degree display system. The alignment techniques for conventional 360-degree display systems, which use a large number of projectors, greatly depend on electronic calibration, which indispensably causes image quality degradation. We propose a systematic approach for the alignment for the light field Mirage, which causes less image quality degradation by taking advantage of the small number of display devices required for the light field Mirage. The calibration technique for light field displays, the image stitching technique, and the brightness matching technique are consecutively performed, and the generation of 360-degree 3D images is verified.

Vision beyond the Field-of-View: A Collaborative Perception System to Improve Safety of Intelligent Cyber-Physical Systems.

Cyber-physical systems (CPSs) that interact with each other to achieve common goals are known as collaborative CPSs. Collaborative CPSs can achieve complex goals that individual CPSs cannot achieve on their own. One of the examples of collaborative CPSs is the vehicular cyber-physical systems (VCPSs), which integrate computing and physical resources to interact with each other to improve traffic safety, situational awareness, and efficiency. The perception system of individual VCPS has limitations on its coverage and detection accuracy. For example, the autonomous vehicle’s sensor cannot detect occluded objects and obstacles beyond its field of view. The VCPS can combine its own data with other collaborative VCPSs to enhance perception, situational awareness, accuracy, and traffic safety. This paper proposes a collaborative perception system to detect occluded objects through the camera sensor’s image fusion and stitching technique. The proposed collaborative perception system combines the perception of surrounding autonomous driving systems (ADSs) that extends the detection range beyond the field of view. We also applied logistic chaos map-based encryption in our collaborative perception system in order to avoid the phantom information shared by malicious vehicles and improve safety in collaboration. It can provide the real-time perception of occluded objects, enabling safer control of ADSs. The proposed collaborative perception can detect occluded objects and obstacles beyond the field of view that individual VCPS perception systems cannot detect, improving the safety of ADSs. We investigated the effectiveness of collaborative perception and its contribution toward extended situational awareness on the road in the simulation environment. Our simulation results showed that the average detection rate of proposed perception systems was 45.4% more than the perception system of an individual ADS. The safety analysis showed that the response time was increased up to 1 s, and the average safety distance was increased to 1.2 m when the ADSs were using collaborative perception compared to those scenarios in which the ADSs were not using collaborative perception.

A Review Over Panoramic Image Stitching Techniques

Using digital images at present are increased rapidly in many fields such as in solving big problems, with the extraordinary appearance of them in all areas of life, in medicine, agriculture, industry, the Internet, and others, where their use is extensive and is considered a source of information for technological progress. One of the important use is stitching the images, also called mosaic images. The stitching images means a grouping of images for the same sense with the overlapping areas to be a panoramic image of high resolution and wide width. With the modification and development of the algorithms used in this field in recent years, it has become one of the essential branches of image processing. There are many applications of stitching, used in maps and satellites, knowledge and positioning, etc. So this summary article will provide a set of image stitching techniques and investigate its use in terms of advantages, disadvantages, and accuracy for each one of them with comparative studies of several research papers in this field for the period of years (2017 – 2020). Therefore, this article may be useful for researchers working in this field to benefit and develop stitching algorithms in terms of discovering features and matching them to create a useful, problem-free, and high-resolution panoramic image.

Robotic-arm-assisted flexible large field-of-view optical coherence tomography

Optical coherence tomography (OCT) is a three-dimensional non-invasive high-resolution imaging modality that has been widely used for applications ranging from medical diagnosis to industrial inspection. Common OCT systems are equipped with limited field-of-view (FOV) in both the axial depth direction (a few millimeters) and lateral direction (a few centimeters), prohibiting their applications for samples with large and irregular surface profiles. Image stitching techniques exist but are often limited to at most 3 degrees-of-freedom (DOF) scanning. In this work, we propose a robotic-arm-assisted OCT system with 7 DOF for flexible large FOV 3D imaging. The system consists of a depth camera, a robotic arm and a miniature OCT probe with an integrated RGB camera. The depth camera is used to get the spatial information of targeted sample at large scale while the RGB camera is used to obtain the exact position of target to align the image probe. Eventually, the real-time 3D OCT imaging is used to resolve the relative pose of the probe to the sample and as a feedback for imaging pose optimization when necessary. Flexible probe pose manipulation is enabled by the 7 DOF robotic arm. We demonstrate a prototype system and present experimental results with flexible tens of times enlarged FOV for plastic tube, phantom human finger, and letter stamps. It is expected that robotic-arm-assisted flexible large FOV OCT imaging will benefit a wide range of biomedical, industrial and other scientific applications.

Applying image registration algorithm combined with CNN model to video image stitching

The purposes are to explore the video image stitching technique of Unmanned Aerial Vehicles (UAVs), expand the application of image registration algorithms and new sensing equipment in video image stitching, and improve the development of video remote sensing image processing. First, how to remotely stitch UAV video images is analyzed. Second, the Scale Invariant Feature Transform (SIFT) algorithm is improved by re-dividing the pixel region and incorporating the vector correlation coefficient. Then, the matching results of the improved SIFT algorithm are compared with those of the traditional SIFT algorithm and Speed Up Robust Feature algorithm. Second, the Random Sample Consensus algorithm is introduced to match the video images accurately, and the matching accuracy of the converted images is verified. Finally, the improved image registration algorithm, the homography matrix estimation model based on convolutional neural network, and the conformation equation of the video sensor are combined to complete the video image stitching of UAVs. Also, the stitching quality of the video image is analyzed. The results show that the improved SIFT algorithm is better than the traditional SIFT algorithm in terms of correct matching and matching time. The visually transformed finely-matched image has a higher matching accuracy rate, and the combination of the two registration algorithms eliminates mismatch points effectively. Compared with the traditional stitching method, the video image stitching method proposed in this study has a higher structural similarity index and edge difference spectrum index, which is feasible and effective. The combination of image registration algorithm with new sensors and deep learning has great application potential in video image stitching.

A fast image-stitching algorithm for characterization of cracks in large-scale structures

Visual inspection of concrete cracks has been widely used in structural health monitoring (SHM). Capturing highresolution images is an effective method to visualize a complete crack, but it is difficult to show a whole crack from a single high-resolution image. One feasible method is using image stitching technique to stitch several images into a complete crack map. However, the current image stitching method is a computationally intensive process. Numerous images are required to cover large-scale structures with sufficient resolution, this can be computationally prohibitive. To address this problem, an improved image stitching method for crack damage evaluation is proposed, which can quickly stitch the crack images without affecting the quality of the stitching or the resulting images. Rather than first stitching the images together and then determining the crack maps, we propose to first develop the crack maps for the individual images and then stitch them together. The proposed method reduces the number of redundant matching points between the original images by combining their characteristics during image stitching, so it can reduce the calculation time without affecting the quality. Also, the results will not be influenced by the image stitching seam, which can reduce the complexity of the algorithm. Several experimental results are provided in this article to demonstrate that the proposed method can reduce the calculation time without affecting the quality of image stitching and have better robustness than the current method in use.

Artefact‐free image stitching via a better normed seam‐cutting energy function

Image stitching, as the important field of computer graphics and vision, has received much attention in recent years. Image stitching techniques are generally decomposed into two phases: image alignment, which aligns target images with the reference images; and image composition, which fixes ghosting and visual artefacts. This work aims to propose a new strategy for the seam-cutting method which provides visually appealing result. Seam-cutting is one of the most influential methods in image composition, which can relieve artefacts and produce plausible results. However, it is observed that the state-of-the-art seam-cutting approaches usually lead to undesirable seams in some challenging scenes. Here, the authors put forward a novel seam-cutting method by defining a new energy function. This method uses 5 / 2 power of L 1 norm as a colour difference which can magnify the weight of colour distinction to avoid undesirable seams and artefacts. The proposed method can be easily implemented. The test images are collected from the public available challenging datasets and taken by ourselves. Experiments demonstrate that the proposed method can create comparable or even better stitching results compared to other state-of-the-art seam-cutting approaches.