HDR-Panorama Design challenges for mobile solutions

Bulletin of Electrical Engineering and Informatics, 2021

This paper presents a methodology for enhancement of panorama images environment by calculating high dynamic range. Panorama is constructing by merge of several photographs that are capturing by traditional cameras at different exposure times. Traditional cameras usually have much lower dynamic range compared to the high dynamic range in the real panorama environment, where the images are captured with traditional cameras will have regions that are too bright or too dark. A more details will be visible in bright regions with a lower exposure time and more details will be visible in dark regions with a higher exposure time. Since the details in both bright and dark regions cannot preserve in the images that are creating using traditional cameras, the proposed system have to calculate one using the images that traditional camera can actually produce. The proposed systems start by get LDR panorama image from multiple LDR images using SIFT features technology and then convert this LDR panorama image to the HDR panorama image using inverted local patterns. The results in this paper explained that the HDR panorama images that resulting from the proposed method is more realistic image and appears as it is a real panorama environment.

This paper covers an implementation of fully automated HDR panorama stitching algorithm. This problem involves image recognition as we need to know which part of panorama to stitch. We used the SIFT algorithm for recognition of the corresponding points; this method is invariant to changes in image scaling, rotation and in illumination. The SIFT algorithm was modified for work with HDR images. Perspective transformations were used to set up new positions of images in the panorama and normalization of luminance was made in order to remove seams from pictures. Results of implementation of the HDR panorama stitching algorithm are presented and discussed. Keywords: HDR panoramas, HDR images, SIFT, local features, perspective transformations.

2007

ABSTRACT Stitching frames from a video sequence has become a good alternative to the traditional method with only a few photographs. The big overlapping between consecutive video-frames makes it possible to construct high-quality panoramas. Most stitching methods require a vast amount of computational resources that make them not suitable for mobile devices. We present a panorama stitching method that is able to create panorama images from a video sequence.

2009 11th IEEE International Symposium on Multimedia, 2009

We present an image labeling approach for merging a set of aligned source images into a composite image by finding optimal seams in the overlapping areas of the source images quickly and using little memory. A minimal-cost path in the overlapping area of two images is found by dynamic programming and used as an optimal seam to label images. The overlapping images are cut along the seam and merged together. A sequential image stitching procedure is integrated with the fast image labeling for producing high-resolution and high-quality panoramic images using large source images under limited computational and memory resources. The approach presents several advantages: the use of dynamic programming optimization for finding the minimal-cost path over adjacent source images guarantees finding the optimal seam and allows images to be merged quickly; ghosting and blurring problems caused by moving objects and small registration errors can be avoided by the optimal seam finding process; the combination of the sequential image stitching procedure with the fast image labeling allows processing large source images for creating high-resolution panoramic images using little memory; the fast labeling process is easy to combine with intensive blending to produce high-quality panoramic images. The method is implemented in our mobile panorama system and runs with good performance on mobile devices.

IEEE Transactions on Consumer Electronics, 2010

We present an efficient image stitching approach for creating high-quality continuous circular 360degree panoramic images. We address the problem of color and luminance correction for long, 360-degree image sequences, where the source images have very different colors and luminance. Color matching is used for minimizing both the color differences of neighboring images and the overall color correction over the whole sequence. We perform gamma correction for the luminance component and linear correction for the chrominance components of the source images. The color consistency problem of 360-degree panoramic images is addressed by color correspondence and color difference distribution processes. We implement the stitching approach in a panoramic imaging system for creating high-resolution and high-quality panoramic images on mobile phones 1 .

Sensors

Image processing on smartphones, which are resource-limited devices, is challenging. Panorama generation on modern mobile phones is a requirement of most mobile phone users. This paper presents an automatic sequential image stitching algorithm with high-resolution panorama generation and addresses the issue of stitching failure on smartphone devices. A robust method is used to automatically control the events involved in panorama generation from image capture to image stitching on Android operating systems. The image frames are taken in a firm spatial interval using the orientation sensor included in smartphone devices. The features-based stitching algorithm is used for panorama generation, with a novel modification to address the issue of stitching failure (inability to find local features causes this issue) when performing sequential stitching over mobile devices. We also address the issue of distortion in sequential stitching. Ultimately, in this study, we built an Android applic...

eCAADe proceedings, 2002

2013

Creating panoramic images in real-time is an expensive operation for mobile devices. Depending on the size of the camera image the mapping of individual pixels into the panoramic image is one of the most time consuming parts. This part is the main focus in this paper and will be discussed in detail. To speed things up and to allow larger images the pixel-mapping process is transferred from the Central Processing Unit (CPU) to the Graphics Processing Unit (GPU). The independence of pixels being projected on the panoramic image allows OpenGL shaders to do the mapping very efficiently. Different approaches of the pixel-mapping process are demonstrated and confronted with an existing solution. The application is implemented for Android phones and works fluently on current generation devices.

Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2018

Head-mounted displays enable a user to view a complete environment as if he/she was there; providing an immersive experience. However, the lighting in a full environment can vary significantly. Panoramic images captured with conventional, Low Dynamic Range (LDR), imaging of scenes with a large range of lighting conditions, can include areas of under-or overexposed pixels. High Dynamic Range (HDR) imaging, on the other hand, is able to capture the full range of detail in a scene. However, HMDs are not currently HDR and thus the HDR panorama needs to be tone mapped before it can be displayed on the LDR HMD. While a large number of tone mapping operators have been proposed in the last 25 years, these were not designed for panoramic images, or for use with HMDs. This paper undertakes a two part subjective study to investigate which of the current, state-of-the-art tone mappers is most suitable for use with HMDs.

SIGGRAPH ASIA 2015 Mobile Graphics and Interactive Applications, 2015

This paper presents an experimental method and apparatus for producing spherical panoramas with high dynamic range imaging (HDRI). Our method is optimized for providing high fidelity augmented reality (AR) image-based environment recognition for mobile devices. Previous studies have shown that a pre-produced panorama image can be used to make AR tracking possible for mobile AR applications. However, there has been little research on determining the qualities of the source panorama image necessary for creating high fidelity AR experiences. Panorama image production can have various challenges that can result in inaccurate reproduction of images that do not allow correct virtual graphics to be registered in the AR scene. These challenges include using multiple angle photograph images that contain parallax error, nadir angle difficulty and limited dynamic range. For mobile AR, we developed a HDRI method that requires a single acquisition that extends the dynamic range from a digital negative. This approach that needs least acquisition time is to be used for multiple angles necessary for reconstructing accurately reproduced spherical panorama with sufficient luminance.