Noise In Binary Image Research Articles

Generic Model of Max Heteroassociative Memory Robust to Acquisition Noise

Associative memories are a significant topic in pattern recognition, and therefore, throughout history, numerous memory models have been designed due to their usefulness. One such model is the associative memory minmax, which is highly efficient at learning and recalling patterns as well as being tolerant of high levels of additive and subtractive noise. However, it is not efficient when it comes to mixed noise. To solve this issue in the associative memory minmax, we present the generic model of heteroassociative memory max robust to acquisition noise (mixed noise). This solution is based on understanding the behavior of acquisition noise and mapping the location of noise in binary images and gray-scale through a distance transform. By controlling the location of the noise, the associative memories minmax become highly efficient. Furthermore, our proposed model allows patterns to contain mixed noise while still being able to recall the learned patterns completely. Our results show that the proposed model outperforms a model that has already solved this type of problem and has proven to overcome existing methods that show some solution to mixed noise. Additionally, we demonstrate that our model is applicable to all associative minmax memories with excellent results.

Image Processing Techniques for COVID-19 Detection in Chest CT

COVID-19 virus is a disease that has spread around the world recently. Early diagnosis of the disease leads us to the opportunity to treat patients faster to reduce its spread in the community. The CT scan image is one of the routines used to diagnose the COVID-19 diseases in an efficient manner and in a faster time. Fractal Dimension which mean (fragmented or irregular) used in wide range of image processing and analysis applications to get the self- similarity of images. To classify textures, combine images, segment and compress images and to generate incredibly complex and good-looking images, the Fractal Dimension method is used. Moreover, Euler method uses features to explain the structural property caused by noise in binary images. It also describes the topological features and analyze the texture of images. In this paper, a method is proposed to obtain the features of CT scan images for COVID-19 by using a hybrid technique called Fractal Dimension Euler (FDE), which merges the two methods of image processing (Fractal Dimension method and Euler Number Method). The two algorithms aim to segment the CT-scan images for the chest to distinguish between the affected and uninfected area of the chest to detect the COVID-19. The results of the proposed approach were very useful in comparison with another approach, the FD method was applied to CT scan images for COVID- 19 using a method called box counting. After that, the Euler method was used to distinguish between foreground and background by using a threshold value. The best threshold value was (255) which achieved the finest result.

Development of a data hiding scheme based on combination theory for lowering the visual noise in binary images

In order to raise the embedding capacity and simultaneously reduce the artifact effect caused by embedding secret messages into binary images, a novel data hiding method based on the combination theory is proposed. In the proposed scheme, a secret position matrix is designed to improve the hiding capacity which is capable of preventing the least distortion based on the combination theory. Our new scheme enables users to conceal more than one bit of secret data by changing at most one pixel in one subimage. We have derived a formula for computing the payload and the possible modification pixels of a block. Compared with the existing schemes in terms of the hiding capacity and the visual artifacts, as our experimental results show, the proposed scheme is capable of providing a better image quality protector even with a more efficient secret data hider.

Automatic Choice of the Threshold of a Grain Filter via Galton–Watson Trees: Application to Granite Cracks Detection

The goal of this paper is the presentation of a post-processing method allowing to remove impulse noise in binary images, while preserving thin structures. We use a grain filter. We propose a method to automatically determine the required threshold using Galton–Watson processes. We present numerical results and a complete analysis on a synthetic image. We end the numerical section considering a specific application to granite samples crack detection: Here we deal with X-tomography images that have been binarized via preprocessing techniques and we want to remove residual impulse noise while keeping cracks and micro-cracks structure.

In-place Algorithm for Erasing a Connected Component in a Binary Image

Removing noise in a given binary image is a common operation. A generalization of the operation is to erase an arbitrarily specified component by reversing pixel values in the component. This paper...

Morphological preprocessing method to thresholding degraded word images

This paper presents a novel preprocessing method based on mathematical morphology techniques to improve the subsequent thresholding quality of raw degraded word images. The raw degraded word images contain undesirable shapes called critical shadows on the background that cause noise in binary images. This noise constitutes obstacles to posterior segmentation of characters. Direct application of a thresholding method produces inadequate binary versions of these degraded word images. Our preprocessing method called Shadow Location and Lightening (SL*L) adaptively, accurately and without manual fine-tuning of parameters locates these critical shadows on grayscale degraded images using morphological operations, and lightens them before applying eventual thresholding process. In this way, enhanced binary images without unpredictable and inappropriate noise can be provided to subsequent segmentation of characters. Then, adequate binary characters can be segmented and extracted as input data to optical character recognition (OCR) applications saving computational effort and increasing recognition rate. The proposed method is experimentally tested with a set of several raw degraded images extracted from real photos acquired by unsophisticated imaging systems. A qualitative analysis of experimental results led to conclusions that the thresholding result quality was significantly improved with the proposed preprocessing method. Also, a quantitative evaluation using a testing data of 1194 degraded word images showed the essentiality and effectiveness of the proposed preprocessing method to increase segmentation and recognition rates of their characters. Furthermore, an advantage of the proposed method is that Otsu’s method as a simple and easily implementable global thresholding technique can be sufficient to reducing computational load.

Enhancing Salt-and-Pepper Noise Removal in Binary Images of Engineering Drawing

Noise removal in engineering drawing is an important operation performed before other image analysis tasks. Many algorithms have been developed to remove salt-and-pepper noise from document images. Cleaning algorithms should remove noise while keeping the real part of the image unchanged. Some algorithms have disadvantages in cleaning operation that leads to removing of weak features such as short thin lines. Others leave the image with hairy noise attached to image objects. In this article a noise removal procedure called TrackAndMayDel (TAMD) is developed to enhance the noise removal of salt-and-pepper noise in binary images of engineering drawings. The procedure could be integrated with third party algorithms' logic to enhance their ability to remove noise by investigating the structure of pixels that are part of weak features. It can be integrated with other algorithms as a post-processing step to remove noise remaining in the image such as hairy noise attached with graphical elements. An algorithm is proposed by incorporating TAMD in a third party algorithm. Real scanned images from GREC'03 contest are used in the experiment. The images are corrupted by salt-and-pepper noise at 10%, 15%, and 20% levels. An objective performance measure that correlates with human vision as well as MSE and PSNR are used in this experiment. Performance evaluation of the introduced algorithm shows better-quality images compared to other algorithms.

A νMOS Vision Chip Based on Cellular-Automaton Processing

A neuron MOS (νMOS) vision chip was designed and fabricated for developing high-speed parallel image-processing systems based on cellular-automaton processing. The chip consists of cellular νMOS circuits that implement two fundamental functions in digital image processing: i) cleaning up noise in binary images and ii) detecting edges in the images, in addition to photosensing and image quantizing. Experimental results reveal that the fabricated chip successfully extracted edges from noisy inputs, which demonstrates the great potential of the νMOS vision chip in future image-processing applications.

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Generation of noise in binary images

We present an algorithm for binary noise generation, with which noisy images with specific noise levels can be generated for any given binary image. It should facilitate the experimental evaluation of different image processing and classification algorithms on binary images.