Due: March 8, 2022 (23:59)
The goal of this assignment is to get you familiarize with the standard camera pipeline of digital photography. You will build your own version of a very basic image processing pipeline (without denoising). You will use this to turn the RAW image into an image that can be displayed on a computer monitor or printed on paper.
For the detailed explanation, please read this document. You can download the data that you use in your assignment from here.
Due: March 22, 2022 (23:59)
The goal of this assignment is to explore high dynamic range (HDR) imaging, color calibration, and tonemapping. HDR imaging can be used to create floating-point precision images that linearly map to scene radiance values. Color calibration ensures that the colors you see in the image match some groundtruth RGB values. Tonemapping algorithms compress the dynamic range of HDR images to an 8-bit range, so that they can be shown on a display. To get full credits, you will need to apply all these steps to an exposure stack.
For the detailed explanation, please read this document. You can download the code and data that you use in your assignment from here.
Due: April 5, 2022 (23:59)
The goal of this assignment is to explore photography with flash/no-flash pairs. In general, getting the lighting in your scene right is one of the most important considerations one needs to take care of when taking a photograph. This can be particularly challenging when the scene you are trying to photograph has a large dynamic range, including both very dark and very bright objects. Previously, we discussed a passive technique for photographing such difficult scenes, using HDR imaging. Here, you will investigate an alternative active technique, where you insert new light into the scene using our camera’s flash.
For the detailed explanation, please read this document. You can download the data that you use in your assignment from here.
Due: April 26, 2022 (23:59)
The purpose of this assignment is to explore the use of deep learning approaches for enhancing low-light images. In general, capturing images in low-light conditions is a challenging task. The level of the signal measured by the camera sensors is generally much lower than the noise in the measurements. Noise present in a low-light image also affects various image characteristics such as fine-scale structures and color balance, further degrading the image quality. Here, you will produce high-quality images from given low-light images by using a deep model which is specifically designed and trained for this task.
For the detailed explanation, please read this document. You can download the starter Jupyter notebook that you use in your assignment from here.