No abstract

No abstract

Motion due to digital camera movement during the image capture process is a major factor that degrades the quality of images and many methods for camera motion removal have been developed. Central to all techniques is the correct recovery of what is known as the Point Spread Function (PSF). A very popular technique to estimate the PSF relies on using a pair of gyroscopic sensors to measure the hand motion. However, the errors caused either by the loss of the translational component of the movement or due to the lack of precision in gyro-sensors measurements impede the achievement of a good quality restored image. In order to compensate for this, we propose a method that begins with an estimation of the PSF obtained from 2 gyro sensors and uses a pair of under-exposed image together with the blurred image to adaptively improve it. The luminance of the under-exposed image is equalized with that of the blurred image. An initial estimation of the PSF is generated from the output signal of 2 gyro sensors. The PSF coefficients are updated using 2D-Least Mean Square (LMS) algorithms with a coarse-to-fine approach on a grid of points selected from both images. This refined PSF is used to process the blurred image using known deblurring methods. Our results show that the proposed method leads to superior PSF support and coefficient estimation. Also the quality of the restored image is improved compared to 2 gyro only approach or to blind image de-convolution results.

Two new global motion estimation methods are proposed. The first one, called Sign Projection (SP) is obtained by modifying the integral projection estimation method using two thresholds for pixel values. The second method, called binary incrementation (BI), is obtained by using only one threshold for generating binary vectors from two images. It is shown that the proposed approaches provide similar motion estimation accuracy with the integral projection (IP) and phase correlation (PC) methods. Also, they have reduced numerical complexity and memory requirements, leading to shorter processing times as well as lower power consumption. The technique is particularly suitable for implementation in consumer devices such as digital video cameras.

This paper presents a method for correcting digital image artifacts that appear in images acquired by digital cameras caused by flash light reflection on out of focus airborne particles. This method makes use of polar coordinates representation of artifacts and it is assumed that they were previously detected.