Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Spatially adaptive color filter array interpolation for noiseless and noisy data: Articles
International Journal of Imaging Systems and Technology - Special Issue on Applied Color Image Processing
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
New efficient methods of image compression in digital cameras with color filter array
IEEE Transactions on Consumer Electronics
Color filter arrays: design and performance analysis
IEEE Transactions on Consumer Electronics
Demosaicing: image reconstruction from color CCD samples
IEEE Transactions on Image Processing
Spatially adaptive wavelet thresholding with context modeling for image denoising
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Color plane interpolation using alternating projections
IEEE Transactions on Image Processing
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
Demosaicing using optimal recovery
IEEE Transactions on Image Processing
Adaptive homogeneity-directed demosaicing algorithm
IEEE Transactions on Image Processing
Demosaicing by successive approximation
IEEE Transactions on Image Processing
Linear demosaicing inspired by the human visual system
IEEE Transactions on Image Processing
Color demosaicking via directional linear minimum mean square-error estimation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Joint demosaicing and denoising
IEEE Transactions on Image Processing
Color Reproduction From Noisy CFA Data of Single Sensor Digital Cameras
IEEE Transactions on Image Processing
Demosaicked image postprocessing using local color ratios
IEEE Transactions on Circuits and Systems for Video Technology
Multiscale LMMSE-based image denoising with optimal wavelet selection
IEEE Transactions on Circuits and Systems for Video Technology
Joint demosaicking and denoising with space-varying filters
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A case for denoising before demosaicking color filter array data
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Multiplicative noise removal via a novel variational model
Journal on Image and Video Processing - Special issue on emerging methods for color image and video quality enhancement
Numerical scheme for efficient colour image denoising
Computers & Mathematics with Applications
Edge adaptive color demosaicking based on the spatial correlation of the bayer color difference
Journal on Image and Video Processing - Special issue on emerging methods for color image and video quality enhancement
Color image demosaicking: An overview
Image Communication
Novel color demosaicking for noisy color filter array data
Signal Processing
Analysis of focus measure operators for shape-from-focus
Pattern Recognition
Hi-index | 0.01 |
Single-sensor digital color cameras use a process called color demosaicking to produce full color images from the data captured by a color filter array (CFA). The quality of demosaicked images is degraded due to the sensor noise introduced during the image acquisition process. The conventional solution to combating CFA sensor noise is demosaicking first, followed by a separate denoising processing. This strategy will generate many noise-caused color artifacts in the demosaicking process, which are hard to remove in the denoising process. Few denoising schemes that work directly on the CFA images have been presented because of the difficulties arisen from the red, green and blue interlaced mosaic pattern, yet a well designed "denoising first and demosaicking later" scheme can have advantages such as less noise-caused color artifacts and cost-effective implementation. This paper presents a principle component analysis (PCA) based spatially-adaptive denoising algorithm, which works directly on the CFA data using a supporting window to analyze the local image statistics. By exploiting the spatial and spectral correlations existed in the CFA image, the proposed method can effectively suppress noise while preserving color edges and details. Experiments using both simulated and real CFA images indicate that the proposed scheme outperforms many existing approaches, including those sophisticated demosaicking and denoising schemes, in terms of both objective measurement and visual evaluation.