This week, I continued to implement the preprocessing section of the computer vision pipeline. The three main goals for the preprocessing are to denoise and increase the contrast and brightness of the the input images. Initially, I used the builtin denoising methods included in the OpenCV library and a simple scaling function to work on contrast and brightness. I found that simply scaling up the image RGB values for contrast and brightness was very rudimentary. Many areas in the image were at risk of overexposure, and the method leaves little room for the preprocessing to adapt for each image. Researching further, another contrast/brightness adjustment method I found was the clahe method which involves normalization of the image values. This sort of system is adaptive for each image and I found it was much better for increasing contrast without overexposing portions of the image.
This week, overall – I wasn’t able to work as much due to assignments in other classes. So the goals for next week include finishing up preprocessing and working model training.
