Surround view camera system is an emerging automotive ADAS (Advanced Driver Assistance System) technology that helps drivers park safer by allowing him/her to see the complete 360-degree surrounding of the vehicle. Such a system normally consists of four to six wide-angle cameras mounted around the vehicle, each facing one direction. From these camera inputs, a composite bird-eye view of the vehicle is synthesized and shown to the driver in real time during parking. One common problem in existing solutions is that the composite view has noticeable boundaries between adjacent views due to different lighting conditions faced by different cameras, as well as different auto-exposure (AE) and auto white balance (AWB) each camera has. This paper describes a computationally efficient method to correct for the photometric misalignment, i.e., brightness and color mismatch, across different cameras. With the proposed methods and proper calibration of the cameras, we achieved a seamlessly stitched 360 degree surround view.

In this paper, we describe an embedded programmable stereoscopic 3D camera system for mobile platforms. With the proliferation of stereoscopic televisions, computer monitors, and even auto-stereoscopic LCDs, as well as a growing inventory of stereoscopic games and movies, there is a growing demand for stereoscopic 3D content creation. We present each of the key components that enable the stereoscopic 3D video capture and playback on the embedded platform, including imaging, codec, graphics, and display subsystems. We describe several unique and new 3D image processing algorithms and explain how they are integrated into the stereo 3D system.

In this paper, we present the adaptive bilateral filter (ABF) for sharpness enhancement and noise removal. The ABF sharpens an image by increasing the slope of the edges without producing overshoot or undershoot. It is an approach to sharpness enhancement that is fundamentally different from the unsharp mask (USM). This new approach to slope restoration also differs significantly from previous slope restoration algorithms in that the ABF does not involve detection of edges or their orientation, or extraction of edge profiles. In the ABF, the edge slope is enhanced by transforming the histogram via a range filter with adaptive offset and width. The ABF is able to smooth the noise, while enhancing edges and textures in the image. The parameters of the ABF are optimized with a training procedure. ABF restored images are significantly sharper than those restored by the bilateral filter. Compared with an USM based sharpening method-the optimal unsharp mask (OUM), ABF restored edges are as sharp as those rendered by the OUM, but without the halo artifacts that appear in the OUM restored image. In terms of noise removal, ABF also outperforms the bilateral filter and the OUM. We demonstrate that ABF works well for both natural images and text images.

In this paper, we present an adaptive bilateral filter (ABF) for sharpness enhancement and noise removal. ABF sharpens an image by increasing the slope of the edges without producing overshoot or undershoot. Our new approach to slope restoration significantly differs from the previous slope restoration algorithms in that ABF does not involve detecting edge orientations or edge profiles. Compared with the bilateral filter, ABF restored images are significantly sharper. Compared with an unsharp mask (USM) based sharpening method the optimal USM (OUM), ABF restored edges are as sharp as those rendered by the OUM, but without halo. ABF also outperforms the bilateral filter and the OUM in noise removal.