TY - JOUR

T1 - Adjoint Bilateral Filter and Its Application to Optimization-based Image Processing

AU - Shirai, Keiichiro

AU - Sugimoto, Kenjiro

AU - Kamata, Sei Ichiro

N1 - Publisher Copyright:
© 2022 K. Shirai, K. Sugimoto and S. Kamata

PY - 2022/4/26

Y1 - 2022/4/26

N2 - This study primarily presents efficient tools for optimization-based image processing using a bilateral filter (BF). Generally, for image restoration, e.g., deblurring, a forward operation and its adjoint operation pair are required to solve inverse problems via iterative approaches such as the gradient method. Image data comprise millions of variables; thus, the operations should be performed as image filters rather than matrix products because of the considerable matrix size. This approach is known as a matrix-free approach, i.e., filter form, because it is executed without explicitly generating an enormous matrix. When BF is incorporated into optimization, its matrix-free adjoint BF is required to solve the optimization problem. This study discusses the matrix-free adjoint BF and its constant-time algorithm to solve optimization problems in a practical time frame. The experimental results demonstrate that the proposed method yields sufficient filtering accuracy for solving inverse problems. Furthermore, BF-based optimization improves accuracy by adjusting the image quality of resultant images.

AB - This study primarily presents efficient tools for optimization-based image processing using a bilateral filter (BF). Generally, for image restoration, e.g., deblurring, a forward operation and its adjoint operation pair are required to solve inverse problems via iterative approaches such as the gradient method. Image data comprise millions of variables; thus, the operations should be performed as image filters rather than matrix products because of the considerable matrix size. This approach is known as a matrix-free approach, i.e., filter form, because it is executed without explicitly generating an enormous matrix. When BF is incorporated into optimization, its matrix-free adjoint BF is required to solve the optimization problem. This study discusses the matrix-free adjoint BF and its constant-time algorithm to solve optimization problems in a practical time frame. The experimental results demonstrate that the proposed method yields sufficient filtering accuracy for solving inverse problems. Furthermore, BF-based optimization improves accuracy by adjusting the image quality of resultant images.

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U2 - 10.1561/116.00000046

DO - 10.1561/116.00000046

M3 - Article

AN - SCOPUS:85129338396

VL - 11

JO - APSIPA Transactions on Signal and Information Processing

JF - APSIPA Transactions on Signal and Information Processing

SN - 2048-7703

IS - 1

M1 - 46

ER -