Seamless stitching of images based on a Haar wavelet 2D integration method

Sevcenco, Ioana S.; Hampton, Peter J.; Agathoklis, P.

doi:10.1109/icdsp.2011.6004934

Cited by 10 publications

(4 citation statements)

References 18 publications

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“…While a full review of blending is beyond the scope of this paper, there are also approaches with watershed segmentation and graph cuts [8] or wavelets [20]. Others focus on compensating colour differences [4], and there are also blending methods based on deep learning [25].…”

Section: Related Workmentioning

confidence: 99%

Image Blending with Osmosis

Bungert¹,

Peter²,

Weickert³

2023

Preprint

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Image blending is an integral part of many multi-image applications such as panorama stitching or remote image acquisition processes. In such scenarios, multiple images are connected at predefined boundaries to form a larger image. A convincing transition between these boundaries may be challenging, since each image might have been acquired under different conditions or even by different devices. We propose the first blending approach based on osmosis filters. These drift-diffusion processes define an image evolution with a non-trivial steady state. For our blending purposes, we explore several ways to compose drift vector fields based on the derivatives of our input images. These vector fields guide the evolution such that the steady state yields a convincing blended result. Our method benefits from the well-founded theoretical results for osmosis, which include useful invariances under multiplicative changes of the colour values. Experiments on real-world data show that this yields better quality than traditional gradient domain blending, especially under challenging illumination conditions.

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Section: Related Workmentioning

confidence: 99%

Image Blending with Osmosis

Bungert¹,

Peter²,

Weickert³

2023

Preprint

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“…To compensate for the resulting uneven bulk-voxel intensity between neighboring subvolumes, image stitching was carried out in the gradient domain using the Haar wavelet twodimensional integration method. 38 A disk-shaped 1% agar gel phantom with radially directed graphite spokes was prepared to evaluate the performance of gradient domain stitching versus spatial domain stitching. The graphite concentration in the spokes was 1 g/L.…”

Section: Image Reconstruction Image Stitching Coregistration and Rementioning

confidence: 99%

Intraoperative photoacoustic screening of breast cancer: a new perspective on malignancy visualization and surgical guidance

et al. 2019

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High re-excision rates in breast-conserving surgery call for a new intraoperative approach to the lumpectomy margin evaluation problem. The unique intraoperative imaging system, presented here, demonstrated the capability of photoacoustic tomography (PAT) to deliver optical sensitivity and specificity, along with over 2-cm imaging depth, in a clinical setting. The system enabled the evaluation of tumor extent, shape, morphology, and position within lumpectomy specimens measuring up to 11 cm in diameter. The investigation included all major breast cancer-related lesions, such as invasive ductal carcinoma (IDC), multifocal IDC, ductal carcinoma in situ and combinations of these variants. Coregistration with established ultrasound (US) technology, as well as comparison to specimen radiography, validated the performance of PAT, which appeared to facilitate better tumor visualization. Contrary to expected PA contrast mechanisms, PAT images of hemoglobin distribution correlated poorly with US-determined tumor location, while hypointense regions in lipid-weighted PAT images were in better agreement with US.

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“…As it extracts 128dimentional it becomes very complex. So, to reduce its complexity, SURF came into picture which improves efficiency with a similar invariant nature to rotation and transformation by making grids around the key-point detected and making histograms around it [10]. The count of histograms increased according to the degree of the…”

Section: Feature Matching 2 Ransac Estimationmentioning

confidence: 99%

Optimizing Image Stitching for Panoramic View

Arora¹,

Singh²

2016

IJERT

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Image Stitching or mosaicing is an important aspect of research in the field of computer vision. It involves various techniques of joining images together to form a mosaic of high resolution. Stitching images generally require complete overlap in order to generate high resolution panoramas. As these panoramas become increasingly popular, there arises a need for the software to create mosaics. These mosaics are used for variety of applications like in digital maps and satellite photos. In recent years various techniques have been generated to do stitching in order to obtain data from images. Such techniques which have been used to successfully combine images are discussed in this paper.

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Seamless stitching of images based on a Haar wavelet 2D integration method

Cited by 10 publications

References 18 publications

Image Blending with Osmosis

Image Blending with Osmosis

Intraoperative photoacoustic screening of breast cancer: a new perspective on malignancy visualization and surgical guidance

Optimizing Image Stitching for Panoramic View

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