Carpet patterns are in two categories: machine-painted and hand-painted. Hand-painted patterns are divided into two groups: before and after discretization. The purpose of this study is Color reduction of hand-painted patterns before discretization. There are some articles about Color reduction of hand-painted carpet patterns after discretization, but so far, there is not an article on patterns before discretization. The proposed algorithm consists of the following steps: image segmentation, finding the Color of each region, Color reduction around the edges and final Color reduction with C-means. For 80 segments of different 20 patterns, the algorithm has an approximate of 96% accuracy. In other words, the Colors of 96% of image pixels are found correctly. The high accuracy of this method is due to its fitness to the application. The proposed method is not fully automatic and requires the total number of Colors as its input.

Automatic reading of carpet patterns Requires To find the original Colors of the pattern in a scanned image. It includes detecting of pattern lines and reducing the number of Colors in the image. Color reduction is done in two steps: Finding the best pallet and mapping the image Colors to the pallet Colors. The accuracy of Color reduction is so important that it may be required to ask for user intervention. The purpose of this study is to provide a new method in automatic Color reduction with high accuracy. To achieve this target, reinforcement learning method is used which yields a 98% accuracy. This is a new method in Color reduction and no one has used it yet. This method is defined with respect to the application and the amount of Color reduction is such that does not degrade the accuracy. Therefore, the resulting pallet has more Colors comparing to the original one. In the work reported in this article, first the grid lines of the pattern are detected. Then a single Color is assigned to each box of the grid. After these steps, through the reinforcement learning method the Color reduction is carried out. The results obtained from applying the proposed algorithm on some sample images are reported and discussed.

Impulsive noise reduction from Color images is a fundamental challenge in image processing, as this noise commonly occurs during the acquisition and transmission of digital images and negatively impacts image quality. In this paper, a novel method for reducing this noise from Color images based on fuzzy filtering is proposed. This approach utilizes fuzzy metrics to evaluate the Color similarity of pixels and their spatial proximity within a fuzzy framework. The Color of each pixel is defined as a fuzzy number, and substitute values for noisy pixels are computed using fuzzy metrics. In this method, Color similarity is assessed using three-component Color vectors (RGB), while spatial proximity is evaluated using fuzzy metrics. The proposed algorithm utilizes a sliding window and is executed in three main steps: (1) converting the pixel values in the window into fuzzy numbers, (2) computing the Color similarity and spatial distance of pixels using two fuzzy distance metrics, and (3) selecting the most suitable pixel based on the obtained criteria and replacing it with the central pixel of the window. The proposed algorithm has been tested on various images, including grayscale and Color noisy images. The results indicate that this approach outperforms existing approaches in noise reduction and detail preservation. This superiority includes higher Peak Signal-to-Noise Ratio (PSNR), higher Structural Similarity Index Measure (SSIM), and more accurate edge and detail restoration.

Chemical and biological treatment of dye-containing effluents is difficult due to the low adsorption level and chemical stability of dyes. Activated carbon is usually used for adsorption of these materials, but because of high prices and recovery problems, researchers are investigating to find available low-cost materials with high adsorption. In this study, bagasse was used for adsorption of Remzol Black B dye and its capability was examined. Variables were concentration, temperature, pH and contact time, and the degree of adsorption was examined in different situations. Then, adsorption isotherm according to Langmuir and Freundlich models were developed. The results showed that the adsorption efficiency depends on pH of solution and to a lower degree on temperature of the solution. Maximum adsorption of Remzol Black B dye by bagasse occurred in the first 15 minutes of contact. The study of adsorption isotherm showed that both models are suitable for description of adsorption of Remzol Black B dye by bagasse. Maximum adsorption of Remzol Black B dye by bagasse was calculated as 7. 51 mg/g. Results of this study showed that bagasse could be used as a low-cost bio adsorbent for the adsorption of Remzol Black B from aqueous solution.

Most Color reduction methods that are based on image clustering in a 3D Color space have extremely high computational costs, especially for large size images. In this paper, a new fast adaptive Color reduction method is proposed which, computationally, is independent of the image size and reduces the pixel depth from 24 bits (used to represent tristimulus values in the most commonly hardware-oriented RGB model) to a maximum of 15 bits. To achieve this purpose, by introducing a new hybrid cost function and using a modified version of the Gravitational Search Algorithm (GSA), an adaptive histogram binning approach has been developed. Although the cube re-quantization accuracy in the histogram binning approach is lower compared to the 3D data clustering method, it leads to a significant reduction in computational cost. In this paper, while taking this advantage, we seek to reduce re-quantization error using the adaptive histogram binning of RGB Color components. Despite a significant reduction in pixel depth, the proposed Color reduction approach, due to the adaptive reduction of image Colors, results in an appropriate Color reduction for a wide variety of images.

The effect of rainbow fungus (Coriolus versiColor), an Iranian native fungus, on the Color reduction of bleach plant effluent was studied. The effective variables in this research were time, temperature, pH, and biomass amount. To investigate the independent effect of each variable on Color reduction, the other variables were kept constant considering the optimum growth conditions of fungi stated in literature. The highest Color reductions were observed after each independent variable treatment; time of 6 days (59%), pH of 3 to 4 (45%), biomass amount of 12 g/l (42%), and temperature of 25oC (50%). Considering the observed results, it can be concluded that the rainbow fungus can efficiently reduce the Color of bleach plant effluent from pulp plant.

Inertial Navigation System (INS) is one of the navigation systems widely used in various land-based, aerial, and marine applications. Among all types of INS, Microelectromechanical System (MEMS)-based INS can be widely utilized, owing to their low cost, lightweight, and small size. However, due to the manufacturing technology, MEMS-based INS suffers from deterministic and stochastic errors, which increase positioning errors over time. In this paper, a new effective noise reduction method is proposed that can provide more accurate outputs of MEMS-based inertial sensors. The intelligent method in this paper is a combined denoising method that combines Wavelet Transform (WT), Permutation Entropy (PE), Support Vector Regression (SVR), and Genetic Algorithm (GA). Firstly, WT is employed to obtain a time-frequency representation of raw data. Secondly, a four-element feature vector is formed. These four features are (1) amplitude of frequency, (2) its ratio to mean of amplitudes of all frequencies, (3) location of frequency in time-frequency representation, and (4) judgment on behaviors of frequency that is obtained by utilizing PE. Thirdly, based on the feature vector, the GA-SVR algorithm predicts amplitudes of all frequencies in the time-frequency representation of the denoised signal. Finally, by employing inverse WT the denoised signal is obtained. In this work, the outputs of the Inertial Measurement Unit (IMU) in ADIS16407 sensor, as a low-cost and MEMS-based INS, have been utilized for data collection. The proposed method has been compared with other noise reduction methods and the achieved results verify superior improvement than other methods.

In Color reduction algorithms the result will be evaluated based on visual or qualitative standards. Evaluation without considering the quantitative standard wouldn't be a complete and accurate evaluation and trends of viewer are very effective on the evaluation. In some articles, the result will be evaluated with MSE. In this standard error the difference between the final images’ pixels Color with first image will be considered as a failure in which is not a suitable technique for evaluating of Color reduction methods. In images Color reduction, if a Color completely be replaced by a Color closed to the original Color it wouldn’t be considered as a failure. If these replacements don’t happen for all of those specific Color pixels, then an error has happened in Color reduction. The disintegration of the resulted Colors from Color reduction algorithm with desired Colors should be considered in presenting the evaluation criteria since this will not be considered in MSE. In some of Color reduction applications such as Color reduction in the carpet cartoons, the final desired pixel Color is specified and presenting the wrong Color will be an error. Therefore, in such applications, the quantitative evaluation based on final Color of each pixel is possible. By presenting criteria for quantitative evaluation, viewer trends wouldn't be considered in evaluation and the possibility of accurate comparison of Color reduction algorithms would take place. In this article, we have presented a technique of quantitative evaluation for Color reduction algorithms. When the final desired Color for pixels are specified, this criteria would work out. To demonstrate the functionality of this quantitative evaluation technique, one of the applications of Color reduction which is Color reduction in carpet cartoons would be discussed. Several methods of Color reduction would be evaluated based on proposed evaluation criteria and reference [42], had the lowest error.