Haruo Hatanaka

Motion blur estimation of handheld camera using regular- and short-exposure image pair

We present in this paper a new image stabilization technology to address motion blurs from camera shake with a regular- and short-exposure image pair taken consecutively. The proposed motion estimation process in this technology adopts a Wiener filter to estimate motion blur using few selected feature corresponding points in the image pair. To obtain a more reliable and precise result, a multi-resolution and morphological image processing technique is then used to further refine the initial estimation. This technology has been applied successfully as an image stabilization function on a consumer digital still camera. The resulting performance is 0.7–1.4 EV exposure time step gain with an average processing time of 3.1 seconds for a 6M pixel image.

Noise-robust image deblurring by blending regular- and short-exposure images

We propose a new image deblurring method using differently exposed image pair. Regular-exposure image has more blur but less noise, while short-exposure image has more noise but less blur. Conventional approaches blend the two images using only good features of them based on the difference between the degradations. Although these approaches are effective under normal conditions, it is difficult to distinguish blur from noise under low light conditions. So we made two improvements to deal with large noise. One is using the gradient information of the regular-exposure image to refine the motion blur detection. The other is that noise on the edges is effectively suppressed considering the edge shapes and the noise levels on each pixel in the blended image. Finally, we implemented our method on the digital still camera and we successfully obtained the higher-quality images with less blur and noise through the simulations as well as the real camera examinations.

An image stabilization technology for digital still camera based on blind deconvolution

We have developed a new image stabilization technology for still images based on blind deconvolution and introduced it into consumer digital still cameras. This technology allows us to reduce processing time and ringing artifacts, the common problems in image deconvolution, without deteriorating deblurring performance.

A User-friendly Video Camera Based on Ergonomic Studies

To design an ergonomically user-friendly video camera, electromyograms (EMGs) at six muscle locations were measured to find an optimal grip angle. Grip angle is an important factor in the usability of general consumer electronic products. The results indicated that vertically-gripped cameras, where the grip angle is large from the axis of the lens alignment, reduced muscular load. Particularly a grip angle from 105deg to 135deg produced little fatigue. In consideration of our results, we developed a compact and lightweight video camera with a grip angle of 105deg based on ergonomic methodology.