Improved Switching-Basedmedian Filter For Impulse Noise Removal

This thesis proposed a new algorithm to reduce impulse noise from digital images. In order to achieve this, thorough literature surveys on impulse noise models and median filtering frameworks have been carried out successfully. The proposed algorithm is based on switching median filtering approaches. The method can be generally divided into two main stages, which are impulse noise detection stage and impulse noise cancellation stage. Modifications towards a well known boundary discriminative detection (BDND) method have been made. First, rather than using any sorting algorithm, the local median values were determined from manipulated local histograms. Next, in the noise detection stage, in addition to the originally proposed intensity distance differential approach, the new method includes intensity height differential approach to reduce false detection rate. Then, instead of using adaptive approach for noise cancellation stage, the proposed method utilizes iterative approach. Broad impulse noise model has been employed for the evaluation process, to investigate the robustness of the method. Based on the evaluations from root mean square error (RMSE), false positive detection rate, false negative detection rate, mean structure similarity index (MSSIM), processing time, and visual inspection, it is shown that the proposed method is the best method when compared with seven other state-of-the art median filtering methods

Noise Reduction For Digital Images Using Median Filtering Technique

Nowadays, the popularity of digital devices increases. However there are still some limitations in digital technology. One of the limitations is the appearance of additive noise in the images that are acquired using long exposure times. Long exposure times are needed in the case when we need to take an image under the conditions that have a low level of illumination, such as during the night time or in a large room such as in an auditorium. Forensics images are also included as the images which need a long exposure time. Noise that is often appears in such conditions is “salt-and-pepper” noise. This noise is called “salt and pepper” because the granular appearance of individual points of noise in the signal. The objective of this project is to find the best way to reduce this type of noise in digital images. From various branched of filter in image processing field, I had chosen median filter as the technique to reduce noise by using Borland C++ compiler 5.5 to complete the project. This nonlinear technique can remove single-point noise caused by experimental errors, or other sampling errors that occur at single points due to bad pixels or other causes. A mean square error measurement was used for comparison of the results. There are 13 different median filtering technique used in the project which are square with causal and iterative median filtering, circle with causal and iterative median filtering, stick with causal and iterative median filtering, square with causal and non-iterative median filtering, circle with causal and non-iterative median filtering, stick with causal and non-iterative median filtering, square with non-causal and iterative median filtering, circle with non-causal and iterative median filtering, stick with non-causal and iterative median filtering, square with non-causal and non-iterative median filtering, circle with non-causal and non-iterative median filtering, stick with non-causal and non-iterative median filtering and progressive switching median filtering. At the end of the project, I suggest the best filter which can used to reduce “salt and pepper” noise in the image is progressive switching median filtering technique. Experiments have shown this technique extensively reduces the noise in an image with no obvious loss of image sharpness

Diaqua­(1,4,8,11-tetra­aza­cyclo­tetra­deca­ne)nickel(II) fumarate tetra­hydrate

The asymmetric unit of the title complex salt, [Ni(C10H24N4)(H2O)2](C4H2O4)·4H2O, comprises half of a nickel(II) complex dication, half of a fumarate dianion and two water mol­ecules. Both the NiII cation and fumarate anion lie on a crystallographic inversion center. The NiII ion in the cyclam complex is six-coordinated within a distorted N4O2 octa­hedral geometry, with the four cyclam N atoms in the equatorial plane and the two water mol­ecules in apical positions. The six-membered metalla ring adopts a chair conformation, whereas the five-membered ring exists in a twisted form. In the crystal packing, inter­molecular O—H⋯O hydrogen bonds between the water molecules and the carboxyl groups of the fumarate anions lead to the formation of layers with R 4 2(8) ring motifs. NiII complex cations are sandwiched between two such layers, being held in place by O—H⋯O, N—H⋯O and C—H⋯O hydrogen bonds, consolidating a three-dimensional network

4-[(2-Hy­droxy-5-nitro­benzyl­idene)amino]­benzene­sulfonamide

The title Schiff base compound, C13H11N3O5S, exists in an E configuration with respect to the C=N double bond. The benzene rings are almost coplanar, making a dihedral angle of 2.82 (6). The sulfonamide group is twisted away from the attached phenyl ring with an N—S—C—C torsion angle of 64.84 (11)°. An intra­molecular O—H⋯N hydrogen bond stabilizes the mol­ecule, generating an S(6) ring motif. In the crystal, inter­molecular N—H⋯O and C—H⋯O hydrogen bonds link the mol­ecules into a three-dimensional network

Aqua­(1,10-phenanthroline-κ2 N,N′)(dl-threoninato-κ2 N,O 1)copper(II) chloride dihydrate

The asymmetric unit of the title compound, [Cu(C4H8NO3)(C12H8N2)(H2O)]Cl·2H2O, contains a complex cation, a chloride anion and two water mol­ecules. The CuII ion has a distorted square-pyramidal coordination geometry formed by one bidentate phenanthroline ligand, one O,N-bidentate dl-threoninate ligand and one apical water mol­ecule. In the crystal structure, inter­molecular O—H⋯O, N—H⋯O, N—H⋯Cl and O—H⋯Cl hydrogen bonds link the components into layers. A single weak inter­molecular C—H⋯O inter­action connects these layers into a three-dimensional network