We review the impact of deep-learning technologies on camera architecture. The function of
a camera is first to capture visual information and second to form an image. Conventionally …

Reflection removal is the challenging problem of removing unwanted reflections that occur
when imaging a scene that is behind a pane of glass. In this paper, we show that most …

The dual-pixel (DP) hardware works by splitting each pixel in half and creating an image
pair in a single snapshot. Several works estimate depth/inverse depth by treating the DP pair …

Most digital cameras use specialized autofocus sensors, such as phase detection, lidar or
ultrasound, to directly measure focus state. However, such sensors increase cost and …

By adopting popular pixel-wise loss, existing methods for defocus deblurring heavily rely on
well aligned training image pairs. Although training pairs of ground-truth and blurry images …

In digital photography, autofocus is a key feature that aids high-quality image capture, and
modern approaches use the phase patterns arising from dual-pixel sensors as important …

Recent works achieve excellent results in dual-pixel defo-cus deblurring task by using
convolutional neural network (CNN), while the scarcity of data limits the exploration and …

Dual-pixel photography is monocular RGB-D photography with an ultra-high resolution,
enabling many applications in computational photography. However, there are still several …

Reducing the defocus blur that arises from the finite aperture size and short exposure time is
an essential problem in computational photography. It is very challenging because the blur …

This paper presents a novel auto-focusing system based on a CMOS sensor containing
pixels with different phases. Robust extraction of features in a severely defocused image is …