Cellular automata-based approach for salt-and-pepper noise filtration

Detection of Tumor Cells in Brain using Cellular Automata with Image Segmentation and Edge Detection.

“…Much of the work has been done in this field and it has been beneficial in so many applications like image recognition, enhancement, morphing of an image, image compression etc. Zubair Jeelani et al [1] proposed an algorithm for detection and removal of noise in an image. FaselQadir et al [2] has proposed the classification of three edge detection rules such as no-edge, sturdy edge and weak edge detection through thorough investigation of linear rules used in CA.…”

Salt & Pepper Noise Reduction using Cellular Automata

“…Noise Filtering from a digital image is a challenge in images. Salt&Pepper noise is an impulse noise, we get this noise in an image often during processing, transmission and acquisition of images [1]. To remove Salt&Pepper noise from an image two filtering techniques can be used: Linear filtering technique and non-linear filtering technique.…”

Modeling and Numerical Validation for an Algorithm Based on Cellular Automata to Reduce Noise in Digital Images

“…Selection of the type of algorithms to be compared considering: reported performance, actuality, available code, number of citations, proposed approach of the algorithm. Some algorithms to consider consist on cellular automata-based algorithmic approaches for noise removal in digital images as Outer Totalistic Cellular Automata (OTCA) [45], and other developments like the presented in [10,[46][47][48][49][50][51][52]; likewise, hybrid methods that incorporate cellular automata and fuzzy logic [32,53], as well as modifications and improvements of median filter as Unsymmetric Trimmed Median Filter (UTMF) [54], median-type noise detectors [34], and implementations using local image statistics [33]. Other approaches could also be considered, including algorithms based on dictionary learning methods [11,12], non-negative matrix factorization [13,14], and robust principal component analysis [15,16].…”

“…Performance metrics considering the operation of the algorithms, in a way that the advantages of each algorithm, can be observed as: processing time, amount of noise removed, image distortion, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Signal-to-Noise Ratio (SNR), Image Enhancement Factor (IEF), and Structural Similarity Index Measure (SSIM), that is a perceptual metric that quantifies image quality degradation caused by the processing; also the Peak Signal-to-Noise Ratio (PSNR) corresponding to the relationship between the maximum possible energy of a signal and the noise that affects it [44,45,53].…”