We propose a novel image denoising strategy based on the correlation in the FREBAS transformed domain. FREAS transform is a kind of multi-resolution image analysis which consists of two different Fresnel transforms. It can decompose images into down-scaled images having the same size and having different frequency bandwidth. Since these decomposed images have similar distribution for the same direction from the center of FREBAS domain. Even the FEBAS signal is hidden in the noise in low SNR image cases, the signal distribution can be estimated using the distribution of the FREBAS signal located nearby the interested position. We develop an collaborative Wiener filter in the FREBAS transformed domain which implement the collaboration of standard deviaton of interested position and that of analogous positions. The experimental results demonstrate that the proposed algorithm improve the SNR in terms of both total SNR and SNR on the edge of images.%我々はフレネル変換を使用した画像の多重解像度解析としてFREBAS変換を提案し，これまで医用画像の雑音除去や鮮鋭化問題に応用してきた．本研究ではFREBAS展開されたスケーリング画像間では，高信号が現れる領域に高い相関がある性質を利用し，注目点近傍だけでなくスケーリング画像間の相似的な位置に存在する信号の分布にも注目する新たな雑音除去フィルタについて検討を行った．雑音除去実験の結果，画像の平均SNRおよび輪郭部と平坦部のSNRともに雑音量が多い場合に大幅に改善されることが示された．また，他の雑音除去法との比較においても提案法の良好な雑音除去特性が示された．
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