PCA Based CFA Denoising And Demosaicking For Digital Image

Mamta.S. Patil; Prof.Vijay K.Patil

CALL FOR PAPERS : Dec-2023

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25-Dec-23

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PCA Based CFA Denoising And Demosaicking For Digital Image

Author(s):

Mamta.S. Patil , D N PATEL COLLEGE OF ENGINEERING-SHAHADA; Prof.Vijay K.Patil, D N PATEL COLLEGE OF ENGINEERING-SHAHADA

Keywords:

Adaptive Denoising, Bayer Pattern, Color Filter Array (CFA), Demosaicking, Principle Component Analysis (PCA).

Abstract:

Principal component analysis (PCA) is an orthogonal transformation that seeks the directions of maximum variance in the data and is commonly used to reduce the dimensionality of the data. In image denoising, a compromise has to be found between noise reduction and preserving significant image details. PCA is a statistical technique for simplifying a dataset by reducing datasets to lower dimensions. It is a standard technique commonly used for data reduction in statistical pattern recognition and signal processing. This paper proposes a denoising technique by using a new statistical approach, principal component analysis with spatial adaptive technique This procedure is iterated second time to further improve the denoising performance, and the noise level is adaptively adjusted in the second stage. 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 paper presents a principle component analysis (PCA) based spatiall-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.