In smartphones there are restrictions for imaging systems like computation capabilities, power and physical size, which have caused usage of relatively low quality camera sensors and modules. To achieve acceptable image quality, low quality images are enhanced and processed with many different algorithms. These algorithms can be executed in different order in the imaging pipeline. Poor order may cause processing blocks executed later to create something undesired to images while in optimal order each processing block should enhance image quality. One very important block is autofocus (AF) statistics calculation block. Poor AF statistics may cause AF algorithm to choose incorrect focus point, which may cause image to become blurred. In addition of producing low quality images, blurry images may cause big problems for later processing blocks in imaging pipeline.
This thesis is done for Intel Finland Oy. The thesis is about studying how much different execution orders of processing blocks affect to accuracy of AF algorithm. To study the subject images were captured from same scene with different focus lens positions and evaluated how easily some AF algorithm could find the best focus point. For that task single statistic was calculated for each differently focused image, which allowed plotting of focus curve. As statistic average amount of edge content in image was used. To calculate it images were filtered with high pass filter. This kind of filtering discards low frequency information and takes to account higher frequency content, which contains mostly information of edges. For evaluating focus curves goodness criteria were developed. Goodness criteria represent the capability of recognizing spike, where image is correctly focused, from focus curve.
In this study it was noticed that decreasing noise made task of AF algorithm significantly easier. Also reasonable downscaling improved situation for AF algorithm, but it also caused time to time something unexpected behavior. On the other hand color correction is something that should be done after AF statistics calculation, because it emphasizes noise
