Impulse Noise Removal Using Soft-computing

Image restoration has become a powerful domain now a days. In numerous real life applications Image restoration is important field because where image quality matters it existed like astronomical imaging, defense application, medical imaging and security systems. In real life applications normally image quality disturbed due to image acquisition problems like satellite system images cannot get statically as source and object both moving so noise occurring. Image restoration process involves to deal with that corrupted image. Degradation model used to train filtering techniques for both detection and removal of noise phase. This degeneration is usually the result of excess scar or noise. Standard impulse noise injection techniques are used for standard images. Early noise removal techniques perform better for simple kind of noise but have some deficiencies somewhere in sense of detection or removal process, so our focus is on soft computing techniques non classic algorithmic approach and using (ANN) artificial neural networks. These Fuzzy rules-based techniques performs better than traditional filtering techniques in sense of edge preservation

An Efficient Image Denoising Approach for the Recovery of Impulse Noise

Image noise is one of the key issues in image processing applications today. The noise will affect the quality of the image and thus degrades the actual information of the image. Visual quality is the prerequisite for many imagery applications such as remote sensing. In recent years, the significance of noise assessment and the recovery of noisy images are increasing. The impulse noise is characterized by replacing a portion of an image's pixel values with random values Such noise can be introduced due to transmission errors. Accordingly, this paper focuses on the effect of visual quality of the image due to impulse noise during the transmission of images. In this paper, a hybrid statistical noise suppression technique has been developed for improving the quality of the impulse noisy color images. We further proved the performance of the proposed image enhancement scheme using the advanced performance metrics

An Efficient Image Denoising Approach for the Recovery of Impulse Noise

Image noise is one of the key issues in image processing applications today. The noise will affect the quality of the image and thus degrades the actual information of the image. Visual quality is the prerequisite for many imagery applications such as remote sensing. In recent years, the significance of noise assessment and the recovery of noisy images are increasing. The impulse noise is characterized by replacing a portion of an image’s pixel values with random values Such noise can be introduced due to transmission errors. Accordingly, this paper focuses on the effect of visual quality of the image due to impulse noise during the transmission of images. In this paper, a hybrid statistical noise suppression technique has been developed for improving the quality of the impulse noisy color images. We further proved the performance of the proposed image enhancement scheme using the advanced performance metrics

Removal of Random Valued Impulsive Noise

In digital Image Processing, removal of noise is a highly demanded area of research.Impulsive noise is common in images which arise at the time of image acquisition and or transmission of images. Impulsive noise can be classified into two categories,namely Salt & Pepper Noise (SPN) and Random Valued Impulsive Noise (RVIN). Removal SPN is easier as compared to RVIN due to its characteristics. The present work concentrates on removal of RVIN from images.Most of the nonlinear filters used in removal of impulsive noise work in two phases,i.e. detection followed by filtering only the corrupted pixels keeping uncorrupted ones intact. Performance of such filters is dependent on the performance of detection schemes. In this work, thrust has been put to devise an accurate detection scheme and a novel weighted median filtering mechanism. The proposed detection scheme utilizes double difference among the pixels in a test window. The difference is computed along four directions namely, horizontal, vertical,and two diagonals to capture the edge direction if any exists. This helps to identify, whether the test pixels under consideration is an edge pixel or a noisy one. Subsequently, the corrupted pixels are passed through in weighted median filter, where more weights are assigned to those pixels which lie in a minimum variance direction among all the four. Extensive simulation has been carried out at various noise conditions and with different standard images. Comparative analysis has been made with existing standard schemes with suitable parameters such as Peak Signal to Noise Ratio (PSNR), fault detection and misses. It has been observed in general that the proposed schemes outperforms its counterparts at low and medium noise conditions and performs at par at high noise conditions with low computational overhead. The low computational requirements have been substantiated with number of operations required for single window operation and overall time required for detection and filtering operation. In addition, every detector utilizes a threshold value which is compared with a predefined computed value to decide whether the pixel under consideration is corrupted. Fixed threshold may perform well for one image at a particular noise condition. However, generalization is not possible for a fixed threshold. Hence, requirement for an adaptive threshold is realized. In the later part of this thesis, we propose an impulsive detection scheme using an adaptive threshold. The adaptive threshold is determined from an Artificial Neural Network (ANN) using various statistical parameters of noisy image like (µ, σ2, µ4) as inputs. The performance of this scheme is also compared with simulation results

On the Development of Impulsive Noise Removal Schemes

Noise Suppression from images is one of the most important concens in digital image porcessing. Impulsive noise is one such noise, which may corrupt images during their acquisitioni or transmission or storage etc.A variety of techniques are reported to remove this type of noise.It is observed that techniques which follow the two satage process of detection of noise and filtering of noisy pixels achieve better performance than others. In this thesis such schemes of impulsive noise detection and filtering thereof are proposed

Detail-preserving switching algorithm for the removal of random-valued impulse noise

© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. This paper presents a new algorithm for the denoising of images corrupted with random-valued impulse noise (RVIN). It employs a switching approach that identifies the noisy pixels in the first stage and then estimates their intensity values to restore them. Local statistics of the textons in distinct orientations of the sliding window are exploited to identify the corrupted pixels in an iterative manner; using an adaptive threshold range. Textons are formed by using an isometric grid of minimum local distance that preserves the texture and edge pixels of an image, effectively. At the noise filtering stage, fuzzy rules are used to obtain the noise-free pixels from the proposed tri-directional pixels to estimate the intensity values of identified corrupted pixels. The performance of the proposed denoising algorithm is evaluated on a variety of standard gray-scale images under various intensities of RVIN by comparing it with state-of-the-art denoising methods. The proposed denoising algorithm also has robust denoising and restoration power on biomedical images such as, MRI, X-Ray and CT-Scan. The extensive simulation results based on both quantitative measures and visual representations depict the superior performance of the proposed denoising algorithm for various noise intensities