Speeding Up the Bilateral Filter: A Joint Acceleration Way.

Area of Science:

• Computer Vision
• Image Processing
• Algorithm Design

Background:

• The computational complexity of the brute-force bilateral filter (BF) is dependent on kernel size.
• Existing acceleration techniques (e.g., 2D box filtering, dimension promotion, shiftability) have limitations and are often used in isolation.
• Combining these techniques presents significant challenges, hindering the development of optimal fast BF implementations.

Purpose of the Study:

• To develop a unified framework for constant-time bilateral filtering.
• To overcome the accuracy and efficiency limitations of existing fast BF methods.
• To enable joint exploitation of multiple acceleration techniques for enhanced performance.

Main Methods:

• A unified framework integrating kernel truncation, N-term approximation, 2D box filtering, dimension promotion, and shiftability property.
• Transformation of the bilateral filter into a set of 3D box filters.
• Linear time computation of the transformed filters.

Main Results:

• The proposed algorithm achieves constant-time bilateral filtering, independent of kernel size.
• Significantly improved filtering accuracy compared to previous methods.
• Maintained or improved efficiency at running time.

Conclusions:

• The unified framework effectively integrates multiple acceleration techniques for bilateral filtering.
• This approach overcomes the deficiencies of isolated techniques, offering superior accuracy and efficiency.
• Represents a novel and effective solution for accelerating bilateral filtering in image processing applications.