sorce code for image restoration in java

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source code for image restoration in java

Image deblurring (or restoration)
Image restoration is a fundamental problem in image processing. How many times has it happened to you, that a possible candid shot came out to be blurred and you cannot shoot the same shot again. Image processing might be a solution.

Image restoration and deblurring has always caught the attention of researchers and industry including practitioner like me. Direct inverse and Weiner filtering are classic methods for restoration in image processing texts as well as few new techniques using wavelet. In latest MATLAB image processing blog, Prof Stanly Reeves explains deblurring with examples, in his introductory and first in series article on deblurring .

This reminds me of another very nice paper presented by Massachusetts Institute of Technology researchers in SIGGRAPH 2006 titled Removing camera shake from a single image.

Both sources will be a nice read for those interested in Image Restoration. For me it’s both, image restoration and photography.

Most digital cameras capture only one color per pixel (red, green or blue), such as in the so-called Bayer pattern. Demosaicing is the process of interpolating the missing color components of every pixel. One line of research focuses on the frequency-domain view of the demosaicing problem, which leads to linear filter schemes to demultiplex the luminance and chrominance signals. Unfortunately, this sometimes results in errors (orange-blue artifacts in the fence and walls in the figure below), that can also be seen in NTSC/PAL television. Wavelet-based demosaicing (Hirakawa et al.) opens up the possibility to jointly handle the demosaicing and denoising problem in the wavelet domain. This is not only elegant, but allows for a lower computational cost, when compared to state-of-the-art techniques. A disadvantage is that restrictive assumptions are imposed on the power spectral density of the image. When these assumptions are invalid (e.g. for high frequency luminance content), luminance and chrominance crosstalk artifacts appear in the image.

In our work, we take a locally adaptive approach to tackle this problem by making use of the directional and analyticity properties of the Complex Dual-Tree wavelets. Surprisingly, this approach does not increase the computational efficiency significantly and gives a very high visual quality.