Restoration of depth-based space-variant blurred images

Yitzhak Yitzhaky; Lior Graham

doi:10.1117/12.2530453

6 September 2019 Restoration of depth-based space-variant blurred images

Yitzhak Yitzhaky, Lior Graham

Proceedings Volume 11137, Applications of Digital Image Processing XLII; 111371L (2019) https://doi.org/10.1117/12.2530453
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

Abstract

Over the last decades, extensive work was done on image de-blurring using different approaches. Most studies assumed that the entire image is equally distorted (space-invariant blur). In such cases, by knowing of finding the single point spread function (PSF) of the distortion, the entire image can be restored using the same distortion PSF. Various attempts have been done also to reconstruct blurred images degraded by a space-variant defocus blur. Here we assume that different areas in the image may contain different levels of Gaussian-like blur, and may include also sharp regions. Gaussian-like blur can approximate distortions such as out-of-focus and long atmospheric path. In the first step we construct a blur map by estimating edge widths at many locations in the image. We assume that the blur map resembles a depth map where the blur severity depends on the distance of the objects from the image, as occurs in limited depth-of-field imaging, focused close to the camera. In the second step the image is divided into a number of non-overlapping layers (regions) according to the blur severity. This means that in each region the blur size is within a relatively small range. Then, in each of these regions we approximate its local PSF according to a best-step-edge based method. Next, each region is de-blurred using a Total Variation reconstruction method. In the final step all the restored regions are combined into a single reconstructed image.

Conference Presentation

Citation Download Citation

Yitzhak Yitzhaky and Lior Graham "Restoration of depth-based space-variant blurred images", Proc. SPIE 11137, Applications of Digital Image Processing XLII, 111371L (6 September 2019); https://doi.org/10.1117/12.2530453

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