Jun Murayama

Image Denoising Based on Overcomplete Topographic Sparse Coding

This paper presents a novel image denoising framework using overcomplete topographic model. To adapt to the statistics of natural images, we impose sparseness constraints on the denoising model. Based on the overcomplete topographic model, our denoising system improves over previous work on the following aspects: multi-category based sparse coding, adaptive learning, local normalization, and shrinkage function. A large number of simulations have been performed to show the performance of the modified model, demonstrating that the proposed model achieves better denoising performance.

Statistically Adaptive Image Denoising Based on Overcomplete Topographic Sparse Coding

This paper presents a novel image denoising framework using overcomplete topographic model. To adapt to the statistics of natural images, we impose both spareseness and topograpgic constraints on the denoising model. Based on the overcomplete topographic model, our denoising system improves the previous work on the following aspects: multi-category based sparse coding, adaptive learning, local normalization, lasso shrinkage function, and subset selection. A large number of simulations have been performed to show the performance of the modified model, demonstrating that the proposed model achieves better denoising performance.

Four-directional pixel-wise polarization CMOS image sensor using air-gap wire grid on 2.5-μm back-illuminated pixels

Polarization information is useful in highly functional imaging. This paper presents a four-directional pixel-wise polarization CMOS image sensor using an air-gap wire grid on 2.5-μm back-illuminated pixels. The fabricated air-gap wire grid polarizer achieved a transmittance of 63.3 % and an extinction ratio of 85 at 550 nm, outperforming conventional polarization sensors. The pixel-wise polarizers fabricated with the wafer process on back-illuminated image sensors exhibit good oblique-incidence characteristics, even with small polarization pixels of 2.5 μm. The proposed image sensor realizes mega-pixel various fusion-imaging applications, such as surface reflection reduction, highly accurate depth mapping, and condition-robust surveillance.