Nao Mishima

Novel frame interpolation method for hold-type displays

Flat panel displays (FPDs) such as a liquid crystal display (LCD) and a plasma display (PDP) have a problem of a motion blur. It is known that the motion blur is caused by the hold-type characteristics of LCD. The problem is improved with a double-rate driving method. In this method, it is necessary to insert the middle frame according to the motions by the objects. The middle frame is created using the frame interpolation technique with the motion estimation and the motion compensation. We proposed a robust hybrid method which consists of several kinds of interpolation method and verified that the hybrid method achieved high image quality by the subjective evaluation.

High-accuracy motion estimation with 4-D recursive search block matching

3-D recursive search block matching is a conventional motion estimation method based on block matching. The algorithm is more efficient and effective than a full-search algorithm. However, the algorithm tends to fail to detect the true motion when the spatiotemporal smoothness of motion is not held. In this paper, we propose a new motion estimation algorithm introducing hierarchical search strategy.

Novel frame interpolation method for high image quality LCDs

Abstract We developed a novel frame interpolation method to interpolate a frame between two successive original frames. Using this method, we are able to apply a double‐rate driving method instead of an impulse driving method where a black frame is inserted between two successive original frames. The double‐rate driving method enables ameliorati on of the motion blur of LCDs caused by the characteristics of human vision without reducing the luminosity of the whole screen. The image quality of the double‐rate driving method was also found to be better than that of an impulse driving method using our motion picture simulator and an actual panel. Our initial model of our frame interpolation method consists of motion estimation with a maximum matching pixel count estimation function, an area segmentation technique, and motion compensation with variable segmentation threshold. Although salt and pepper noise remained in a portion of an object mainly due to inaccuracy of motion estimation, we verified the validity of our method and the possibility of improvement in hold‐type motion blurring.

Image Denoising with Sparsity Distillation

We propose a new image denoising method with shrinkage. In the proposed method, small blocks in an input image are projected to the space that makes projection coefficients sparse, and the explicitly evaluated sparsity degree is used to control the shrinkage threshold. On average, the proposed method obtained higher quantitative evaluation values (PSNRs and SSIMs) compared with one of the state-of-the-art methods in the field of image denoising. The proposed method removes random noise effectively from natural images while preserving intricate textures.