Quantifying the contrast of objects in a complex image

Yelmanov, Sergei; Romanyshyn, Yuriy

doi:10.1109/elnano50318.2020.9088760

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…Therefore, in this study, we also incorporate BIQUE [50] and NIQE [51] as "no-reference" evaluation metrics (Table 3). These two evaluation methods are noreference image quality assessment metrics dedicated to quantifying the quality of images without the need for any additional reference data [52,53]. It is noteworthy that RIS-DMRN produces higher quality results under these blind image quality evaluation metrics.…”

Section: Quantitative Comparison Of Image Rectificationmentioning

confidence: 99%

Rectification for Stitched Images with Deformable Meshes and Residual Networks

Fan,

Mao,

et al. 2024

Applied Sciences

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Image stitching is an important method for digital image processing, which is often prone to the problem of the irregularity of stitched images after stitching. And the traditional image cropping or complementation methods usually lead to a large number of information loss. Therefore, this paper proposes an image rectification method based on deformable mesh and residual network. The method aims to minimize the information loss at the edges of the spliced image and the information loss inside the image. Specifically, the method can select the most suitable mesh shape for residual network regression according to different images. Its loss function includes global loss and local loss, aiming to minimize the loss of image information within the grid and global target. The method in this paper not only greatly reduces the information loss caused by irregular shapes after image stitching, but also adapts to different images with various rigid structures. Meanwhile, its validation on the DIR-D dataset shows that the method outperforms the state-of-the-art methods in image rectification.

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Section: Quantitative Comparison Of Image Rectificationmentioning

confidence: 99%

Rectification for Stitched Images with Deformable Meshes and Residual Networks

Fan,

Mao,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

Rectification for Stitched Images with Deformable Meshes and Residual Networks

Fan,

Mao,

et al. 2024

Preprint

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Image stitching is a crucial aspect of image processing. However, factors like perspective and environment often lead to irregular shapes in stitched images. Cropping or completion methods typically result in substantial loss of information. This paper proposes a method for rectifying irregularly images into rectangles using deformable meshes and residual networks. The method utilizes a convolutional neural network to quantify rigid structures of images. Choosing the most suitable mesh structure based on the extraction results, offering options such as triangular, rectangular, and hexagonal. Subsequently, the irregularly image, predefined mesh structure, and predicted mesh structure are input into a wide residual neural network for regression. The loss function comprises local and global, aimed at minimizing the loss of image information within the mesh and global target. This method not only significantly reduces information loss during rectification but also adapting to different images with various rigid structures. Validation on the DIR-D dataset shows this method outperforms state-of-the-art methods in image rectification.

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Quantifying the contrast of objects in a complex image

Cited by 2 publications

References 4 publications

Rectification for Stitched Images with Deformable Meshes and Residual Networks

Rectification for Stitched Images with Deformable Meshes and Residual Networks

Rectification for Stitched Images with Deformable Meshes and Residual Networks

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