Dietary intake, the process of determining what someone eats during the course of a day, provides valuable
insights for mounting intervention programs for prevention of many chronic diseases such as obesity and cancer.
The goals of the Technology Assisted Dietary Assessment (TADA) System, developed at Purdue University, is
to automatically identify and quantify foods and beverages consumed by utilizing food images acquired with
a mobile device. Color correction serves as a critical step to ensure accurate food identification and volume
estimation. We make use of a specifically designed color checkerboard (i.e. a fiducial marker) to calibrate the
imaging system so that the variations of food appearance under different lighting conditions can be determined.
In this paper, we propose an image quality enhancement technique by combining image de-blurring and color
correction. The contribution consists of introducing an automatic camera shake removal method using a saliency
map and improving the polynomial color correction model using the LMS color space.