Motion Deblurring

Abstract

The main contribution of this work is that we extend the cepstral analysis based PSF estimation to handle non-linear motion PSF. To introduce the cepstral approach for non-linear motion PSF estimation, we will analyze the cepstral behavior of non-linear motion. Then, I propose a non-linear motion PSF estimation method based on the analyzed cepstral behavior. The proposed method is validated with both synthesized images and real world images. The experimental results show that cepstral analysis based method can work for non-linear motion PSF estimation.

Results

Simulation results for piecewise linear motion: Examples of the PSF shape estimation experiment. From top to bottom, latent images, blurred images, restored images by estimated PSFs, restored images by ground truth PSFs, and zoomed up of the restored images are shown. Red framed figures in restored images are PSFs used for deconvolution (for better visualization, we enlarge the PSFs 3 times the normal size). From left to right, PSF size is increasing.

Simulation results for non-linear motion: Experimental results: Restored images of the entire method. From left to right, MOUNTAIN, WOMAN, SHIP cases are shown with NCC values between the restored images. From top to bottom, original images, blurred images, restored images by estimated PSF, restored images by ground truth PSF, and zoomed up of the restored images are shown. Red framed figures in restored images are PSFs used for deconvolution (for better visualization, we enlarge the PSFs 3 times the normal size).

Real image results for piecewise linear motion: PLM PSF estimation for real world images. From top to bottom, TREE, FLOWER, DOLL, and TEXT scenes are shown. From left to right, blurred images, estimated PSFs, restored images. Captions of middle column images denotes estimated PSF size.

Real image results for non-linear motion: Experimental results: Restored images of the real world experiment. From left to right, DOLL, ORANGE, SIGN BOARD, and TEXT scenes are shown with the image resolution. From top to bottom, blurred images, restored images by our method, restored images by Fergus's method [Fergus et al., 2006], restored images by deconvblind [MathWorks], and Zoom up of restored images are shown. Red framed figures in restored images are PSFs used for deconvolution (for better visualization, we enlarge the PSFs 3 times the normal size) and each caption of restored images denotes the size of the estimated PSF. Other framed figures correspond to zoomed up regions of restored images.

Publication

Please contact me if you want any pre-prints.

Yuji Oyamada, "Cepstral Analysis based Non-Linear Motion PSF Estimation," Ch.3, Ph.D. Thesis, Keio University, 2011, [pdf].
Yuji Oyamada, Haruka Asai, and Hideo Saito, "Blind Deconvolution for A Curved Motion Based on Cepstral Analysis," IPSJ Transactions on Computer Vision and Applications (CVA), Vol. 3, pp. 32-43, 2011, [bib, pdf].
Haruka Asai, Yuji Oyamada, and Hideo Saito, "Cepstral Analysis Based Blind Deconvolution for Motion Blur," IEEE International Conference on Image Processing (ICIP), pp. 1153-1156, 2010, [bib, pdf].
浅井晴香, 小山田雄仁, 斎藤英雄, 太田垣康二, 江口満男, "Blind Deconvolutionのための劣化画像のケプストラム解析." 研究報告コンピュータビジョンとイメージメディア (CVIM), vol. 2010, no. 2, pp. 1-8, 2010.
浅井晴香, 小山田雄仁, 斎藤英雄, 太田垣康二, 江口満男, "ケプストラム解析を用いたブレ画像のBlind Deconvolution." 画像の認識・理解シンポジウム (MIRU), pp. 1043-1050, 2009.
浅井晴香, 小山田雄仁, 斎藤英雄, 太田垣康二, 江口満男, "ケプストラム解析を用いたブレ画像のBlind Deconvolution." 研究報告コンピュータビジョンとイメージメディア (CVIM), vol. 2009, no. 26, pp. 1-8, 2009.
小山田雄仁, 浅井晴香, 斎藤英雄, 太田垣康二, 江口満男, "ケプストラムに基づいたブレ画像補正." ビジョン技術の実利用ワークショップ (ViEW), pp. 263-268, 2008.
Yuji Oyamada, Hideo Saito, Koji Ootagaki, and Mitsuo Eguchi, "Cepstrum based Blind Image Deconvolution," International Workshop on Vision, Communications and Circuits (IWVCC), pp. 197-200, 2008, [bib].
小山田雄仁, 斎藤英雄, 太田垣康二, 江口満男, "劣化画像のケプストラムを利用した手ブレ補正." 電子情報通信学会技術研究報告, vol. 108, no. 198, pp. 147-152, 2008.

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