# High-Quality Fast non-Blind Deconvolution Using Sparse Adaptive Priors

(Supplementary Materials)

This web page contain examples of deconvolution using the method proposed in our paper and a comparison with state of the art ones.

Important: These materials are best viewed in a web browser that is not Internet Explorer (IE), since it cannot resize the images properly. Furthermore, in IE you might need to allow ActiveX content to display the images.

Table of Contents:

- Our method: Example 1
- Our method: Example 2
- Our method: Example 3
- Methods Comparison
- Avoiding ringing artifacts at image borders
- Kernel used in the examples
- Natural Images: Example 1
- Natural Images: Example 2
- References

### Our method: Example 1

(Click on an image to view in full size)

Image (b) was obtained by convolving image (a) with the 19 x 19 blur kernel of Krishnan and Fergus and adding 1% of Gaussian noise. Image (c) was obtained by deconvolving image (b) using our method.

### Our method: Example 2

(Click on an image to view in full size)

Image (b) was obtained by convolving image (a) with the 19 x 19 blur kernel of Krishnan and Fergus and adding 1% of Gaussian noise. Image (c) was obtained by deconvolving image (b) using our method.

### Our method: Example 3

(Click on an image to view in full size)

Image (b) was obtained by convolving image (a) with the 19 x 19 blur kernel of Krishnan and Fergus and adding 1% of Gaussian noise. Image (c) was obtained by deconvolving image (b) using our method.

## Methods Comparison

### Original image, kernel and blurred image

(Click on an image to view in full size)

Image (c) was obtained by convolving image (a) with the 19 x 19 blur kernel of Krishnan and Fergus (b) and adding 1% of Gaussian noise.

### Deconvolved images

The following images were deconvolved using the acompanying script: '/scripts/demo_our_method.m'.

## Avoiding ringing artifacts at image borders when performing deconvolution in frequency domain.

The following images illustrate our padding method to avoid artifacts when deconvolving in frequency domain

Blurred image was obtained by convolving image (a) with the 19 x 19 blur kernel of Krishnan and Fergus and adding 1% of Gaussian noise.

## Kernel used in the preceding examples

19 x 19 blur kernel of Krishnan and Fergus [1] used in the preceding examples, (Click on an image to view in full size)

## Natural Images Examples

### Original image, kernel and deconvolved image

(Click on an image to view in full size)

Example of camera-motion deblurring. (a) Blurry photograph. (b) Corresponding camera-motion kernel provided by Shan et al's. [4]. (c) Deblurred result produced by our technique. Note the clearly visible edges and spots on the leaves.

### Original image, kernel and deconvolved image

(Click on an image to view in full size)

Another example of camera-motion deblurring. (a) Input photograph. (b) Corresponding camera-motion kernel by Shan et al [4]. (c) Recovered image obtained with our technique. Note the considerable improvement in sharpness.

## References

[1] KRISHNAN, D., AND FERGUS, R. 2009. Fast image deconvolution using hyper-laplacian priors. In Advances in Neural Information Processing Systems 22, Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta, Eds. 1033–1041.

[2]LEVIN, A., FERGUS, R., DURAND, F., AND FREEMAN, W. T. 2007. Deconvolution using natural image priors. ACM Trans. Graphics 26, 3, 0–2. Suppl. materials.

[3] LUCY, L. B. 1974. An iterative technique for the rectification of observed distributions. The Astronomical Journal 79, 745+.

[4] SHAN, Q., JIA, J., AND AGARWALA, A. 2008. High-quality motion deblurring from a single image. ACM Transactions on Graphics (SIGGRAPH) 27.

[5] WANG, Y., YANG, J., YIN, W., AND ZHANG, Y. 2008. A new alternating minimization algorithm for total variation image reconstruction. SIAM J. Img. Sci. 1, 3, 248–272.

[6] ZHOU, C., AND NAYAR, S. K. 2009. What are good apertures for defocus deblurring? In ICCP, 1–8.