In this paper, we investigate the use of the non-local means (NLM) denoising approach in the context of image
deblurring and restoration. We propose a novel deblurring approach that utilizes a non-local regularization
constraint. Our interest in the NLM principle is its potential to suppress noise while effectively preserving edges
and texture detail. Our approach leads to an iterative cost function minimization algorithm, similar to common
deblurring methods, but incorporating update terms due to the non-local regularization constraint. The dataadaptive
noise suppression weights in the regularization term are updated and improved at each iteration, based
on the partially denoised and deblurred result. We compare our proposed algorithm to conventional deblurring
methods, including deblurring with total variation (TV) regularization. We also compare our algorithm to
combinations of the NLM-based filter followed by conventional deblurring methods. Our initial experimental
results demonstrate that the use of NLM-based filtering and regularization seems beneficial in the context of
image deblurring, reducing the risk of over-smoothing or suppression of texture detail, while suppressing noise.
Furthermore, the proposed deblurring algorithm with non-local regularization outperforms other methods, such
as deblurring with TV regularization or separate NLM-based denoising followed by deblurring.
This paper describes a framework for automatic generation of an image processing algorithm that consists of preprocessing, feature extraction, classification and algorithm evaluation modules based on machine learning. With a view to applying the generated algorithm to industrial visual inspection system, we intend to offer a framework model equipped with the below-mentioned features. Also, we want to report on the experimental result of the offered model.
1.Automatically generate by machine learning an image processing algorithm to extract regions that have same characteristics as specified by users.
2.Generate in particular a high-precision image processing algorithm, improving the level of statistical separation between true and false defects that may cause a deterioration factor in classification accuracy.
3.Optimize an image improving filter sequence in preprocessing modules by means of GA (Genetic Algorithm).
We developed a Mobile Unit which purpose is to support memory retrieval of daily life. In this paper, we describe the two characteristic factors of this unit. (1)The behavior classification with an acceleration sensor. (2)Extracting the difference of environment with image processing technology. In (1), By analyzing power and frequency of an acceleration sensor which turns to gravity direction, the one's activities can be classified using some techniques to walk, stay, and so on. In (2), By extracting the difference between the beginning scene and the ending scene of a stay scene with image processing, the result which is done by user is recognized as the difference of environment. Using those 2 techniques, specific scenes of daily life can be extracted, and important information at the change of scenes can be realized to record. Especially we describe the effect to support retrieving important things, such as a thing left behind and a state of working halfway.