With the introduction of high mega-pixel image sensors and large focal length lenses in today's consumer level digital
still cameras, single-shot passive auto-focus (AF) performance in terms of speed and accuracy remains to be a critical
issue among camera manufacturers. To address the AF performance issue, this paper covers the real-time
implementation of a previously developed modified rule-based single-shot AF search method on the Texas Instruments
TMS320DM350 processor. It is shown that a balance between AF speed and accuracy is needed to meet the real-time
constraint of the digital camera system. Performance results indicate that this solution outperforms the standard global
search method in terms of AF speed and accuracy.
KEYWORDS: Discrete wavelet transforms, Digital signal processing, Digital cameras, Cameras, Wavelets, Video, Digital imaging, Algorithm development, Computer programming, Light sources
Auto white balancing (AWB) involves the process of making white colors to appear as white under different illuminants in digital imaging products such as digital still cameras. This paper presents a computationally efficient auto white balancing algorithm for real-time deployment in imaging products. The algorithm utilizes DWT (discrete wavelet transform) to perform multi-scale clustering (MSC), thus generating a computationally efficient implementation of the original MSC algorithm. The paper also discusses the steps taken to allow running this algorithm in real-time on a digital camera processor. The results of an actual implementation on the Texas Instruments TMS320DM320 processor are provided to illustrate the effectiveness of this algorithm in identifying an illuminant as compared to the widely used gray-world auto white balancing algorithm.
This paper discusses the real-time implementation of a fast and accurate auto-focus method on the Texas Instruments DM270, a programmable processor designed specifically for digital still cameras. The DM270's programmable auto-focus hardware filter is utilized to obtain a sharpness function from a captured image. This function is then used to drive a rule-based search algorithm, which varies the focusing step size depending on the slope of the sharpness function. This leads to faster focusing speeds as compared to the standard global search algorithm. A wide variety of filters are tested by examining their performances in terms of focusing accuracy. The results show that the filters approximating the first derivative operator generate the best focusing accuracy under various focusing conditions.
Conference Committee Involvement (3)
Real-Time Image and Video Processing
17 April 2014 | Brussels, Belgium
Real-Time Image and Video Processing 2011
24 January 2011 | San Francisco Airport, California, United States
Real-Time Image and Video Processing
16 April 2010 | Brussels, Belgium
Course Instructor
SC809: Real-Time Image and Video Processing
The course provides a much-needed treatment on the implementation aspects of real-time image and video processing systems. It brings together in one place the guidelines, strategies and methodologies for taking an image or video processing algorithm from a research environment to real-time implementation on a resource constrained hardware platform. Carefully selected, relevant examples from the literature will be presented to illustrate the concepts. The participants will be introduced to a wide variety of strategies and tools which they can then use in designing a real-time image or video processing system of interest.
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