Joint Content-Adaptive Dictionary Learning And Sparse Selective Extrapolation For Cross-Spectral Image Reconstruction

Genser, Nils; Seiler, Jurgen; Kaup, André

doi:10.1109/icip40778.2020.9191038

Cited by 6 publications

(4 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, similar blocks are searched in the rectangle spanned by 16 pixels to the left, right, up, and down. The parameters of the competitors [9], [10], and [12] are set to the values given in the corresponding paper.…”

Section: Resultsmentioning

confidence: 99%

“…NOCS handles this situation much better, especially since many tiles are repeated in the neighborhood. CASE [12] Proposed NOCS CASE [12] Proposed NOCS CASE [12] Proposed NOCS Fig. 5.…”

Section: Resultsmentioning

confidence: 99%

“…Thus, these methods exploit spatio-spectral correlation. The state of the art for these type of algorithms is Content-Adaptive Selective Extrapolation (CASE) [12], which is based on estimating a local linear regression model and predicts the missing values within the block by applying this model. Unfortunately, due to the block-based processing approach, blocks with high frequency content are often modeled inaccurately due to the rapid change between different pixels for the linear color model.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spatio-spectral Image Reconstruction Using Non-local Filtering

Sippel

Seiler

Kaup

2021

2021 International Conference on Visual Communications and Image Processing (VCIP)

Self Cite

View full text Add to dashboard Cite

In many image processing tasks it occurs that pixels or blocks of pixels are missing or lost in only some channels. For example during defective transmissions of RGB images, it may happen that one or more blocks in one color channel are lost. Nearly all modern applications in image processing and transmission use at least three color channels, some of the applications employ even more bands, for example in the infrared and ultraviolet area of the light spectrum. Typically, only some pixels and blocks in a subset of color channels are distorted. Thus, other channels can be used to reconstruct the missing pixels, which is called spatio-spectral reconstruction. Current state-of-the-art methods purely rely on the local neighborhood, which works well for homogeneous regions. However, in highfrequency regions like edges or textures, these methods fail to properly model the relationship between color bands. Hence, this paper introduces non-local filtering for building a linear regression model that describes the inter-band relationship and is used to reconstruct the missing pixels. Our novel method is able to increase the PSNR on average by 2 dB and yields visually much more appealing images in high-frequency regions.

show abstract

Section: Resultsmentioning

confidence: 99%

“…NOCS handles this situation much better, especially since many tiles are repeated in the neighborhood. CASE [12] Proposed NOCS CASE [12] Proposed NOCS CASE [12] Proposed NOCS Fig. 5.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spatio-spectral Image Reconstruction Using Non-local Filtering

Sippel

Seiler

Kaup

2021

2021 International Conference on Visual Communications and Image Processing (VCIP)

Self Cite

View full text Add to dashboard Cite

show abstract

“…The Camera Array for Multi-Spectral Imaging (CAMSI) [9] has nine channels and is able to record high-resolution multispectral video sequences. Note that by using the algorithms of [10] and [11], all views are reconstructed to the center view. camera.…”

Section: Introductionmentioning

confidence: 99%

Structure-preserving spectral reflectance estimation using guided filtering

Sippel¹,

Seiler²,

Genser³

et al. 2020

J. Opt. Soc. Am. A

Self Cite

View full text Add to dashboard Cite

Light spectra are a very important source of information for diverse classification problems, e.g., for discrimination of materials. To lower the cost of acquiring this information, multispectral cameras are used. Several techniques exist for estimating light spectra out of multispectral images by exploiting properties about the spectrum. Unfortunately, especially when capturing multispectral videos, the images are heavily affected by noise due to the nature of limited exposure times in videos. Therefore, models that explicitly try to lower the influence of noise on the reconstructed spectrum are highly desirable. Hence, a novel reconstruction algorithm is presented. This novel estimation method is based on the guided filtering technique that preserves basic structures, while using spatial information to reduce the influence of noise. The evaluation based on spectra of natural images reveals that this new technique yields better quantitative and subjective results in noisy scenarios than other state-of-the-art spatial reconstruction methods. Specifically, the proposed algorithm lowers the mean squared error and the spectral angle up to 46% and 35% in noisy scenarios, respectively. Furthermore, it is shown that the proposed reconstruction technique works out of the box and does not need any calibration or training by reconstructing spectra from a real-world multispectral camera with nine channels.

show abstract

AIoT-Enabled Precision Agriculture for Sustainable Crop Disease Management: Advancing SDGs Through Graph Attention Neural Networks

Kusharki,

Muhammad-Bello

2024

Lecture Notes on Data Engineering and Communications Technologies

View full text Add to dashboard Cite

Joint Content-Adaptive Dictionary Learning And Sparse Selective Extrapolation For Cross-Spectral Image Reconstruction

Cited by 6 publications

References 17 publications

Spatio-spectral Image Reconstruction Using Non-local Filtering

Spatio-spectral Image Reconstruction Using Non-local Filtering

Structure-preserving spectral reflectance estimation using guided filtering

AIoT-Enabled Precision Agriculture for Sustainable Crop Disease Management: Advancing SDGs Through Graph Attention Neural Networks

Contact Info

Product

Resources

About