Salt and Pepper Noise

In this paper, a new adaptive weight algorithm is developed for the removal of salt and pepper noise particularly in satellite images. Satellite images are corrupted when acquired by a defective sensor or when transmitted through a faulty channel. Salt and pepper noise can be commonly found in the sensor or transmission channel during acquisition and transmission of digital satellite images. This algorithm can be worked in two phases. During the first phase, detecting the noise pixel according to the correlations among image pixels and in second phase adaptive weight algorithm is used to remove the noise. Simulation results based on Matlab show that this method can eliminate the salt and pepper noise in satellite images efficiently and preserves edges and details information of the image. Also this method can remove noise for highly contaminated image with noise level as large as 90%, it still can get the better performance

In the field of image processing, image restoration is the most essential task. Image often gets corrupted due to which there is presence of noise in the image. Generally median filter is used to remove the presence of such noise but median filter works fine for about intensity of 20% noise in the image. So in order to get a better image restoration we can use another image restoration technique which is adaptive median filtering which works very well for noise intensity beyond 20% .The benefit of adaptive filter over median filter is that it does not erode away edges or small details in the image.

In image processing, restoration and noise reduction is expected to improve the qualitative inspection of the image and the performance of quantitative image analysis techniques. Different approaches for image restoration have been proposed, each of which has its own advantages and limitation. This paper combines the adaptive median filtering technique and the non-local means filtering algorithm for image denoising salt and pepper noise to yield a considerably high PSNR. The Non-Local means filter assumes that an image contains an extensive amount of self-similarity and exploits it to denoise an image. The analysis of the statistical results is presented in
simulations done using MATLAB to demonstrate the advantages and limitations of using the proposed method over other existing techniques.

In this project, an efficient method is proposed to remove the high level of salt and pepper noise in image using median filter. The objective of any noise removal technique is to remove the noise completely from the image, such that the resulting image is better than original image. Salt and pepper noise can be added to image during acquisition process. This noise is removed by the proposed method. This plays a vital role in digital images. The proposed approach is used to improve the quality of the image by removing high tone salt and pepper noise. The time consumption for removing salt and pepper noise in image can be reduced in this approach. The verification is based on filtered images. The resultant image is the converted into binary code. Then the binary code of the resultant image is simulated in Xilinx software. Then it is implemented in FPGA kit using Xilinx software. The resultant image will have good noise removal efficiency.

The paper purposes the removal of noise in digital gray scale images that often observed in scanned documents. Generally, Data i.e. picture, text can be contaminated by an additive noise during the process of scanning. This methodology prevents this type of noise known as Salt and Pepper noise (SP Noise) which causes white and black spots on the original image. We are designing a new algorithm for removal of these white and black spots after the knowledge of Median Filter, Adaptive Filter and the new proposed algorithm will definitely protect the image from noise and distortion. Firstly, Adaptive Histogram Equalization is done on the original image. Secondly apply Adaptive contrast Enhancement Technique on the resultant image. After Contrast Enhancement we apply filters Such as Homomorphic filtering. These filters are applied sequentially on distorted images for removing the image.

Impulse noise is often introduced into images during transmission contaminating the images due to channel errors. Based on the noise values, impulse noise can be classified as Fixed Valued Impulsive Noise or Salt and Pepper Noise(SPN) and Random Valued Impulsive Noise(RVIN). In this paper, an inpainting algorithm is presented based on Directional Weighted Median(DWM) Filter to denoise both the noises caused due to image transmission over multipath fading channel. The algorithm diffuses median value of pixels from the exterior area into the inner area and thus preserves the edges and fine details. The random valued impulsive noise and salt and pepper noise due to wireless channel modeled are simulated using MATLAB channel objects. The detection algorithm combined with the image correction based on DWMF shows better performance in terms of Peak Signal-to-

In this study, we propound a salt-and-pepper noise (SPN) removal method, i.e. Adaptive Cesáro Mean Filter (ACmF), and provide some of its basic notions. We then apply ACmF to several test images whose noise densities range from 10% to 90%: 15 traditional test images (Baboon, Boat, Bridge, Cameraman, Elaine, Flintstones, Hill, House, Lake, Lena, Living Room, Parrot, Peppers, Pirate, and Plane) and 40 test images, provided in the TESTIMAGES Database. Afterwards, we compare ACmF with the state-of-art methods, such as Adaptive Weighted Mean Filter (AWMF), Different Applied Median Filter (DAMF), and Noise Adaptive Fuzzy Switching Median Filter (NAFSMF). The results by The Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) show that ACmF performs better than the methods mentioned above. Moreover, we also compare the running time data of these algorithms. These results show that ACmF outperforms the methods except for DAMF. We finally discuss the need for further research.

Abstract: In this paper, we propose a new method for impulse noise removal from images. It uses the sparsity of images in the Discrete Cosine Transform (DCT) domain. The zeros in this domain give us the exact mathematical equation to reconstruct the pixels that are corrupted by random-value impulse noises. The proposed method can also detect and correct the corrupted pixels. Moreover, in a simpler case that salt and pepper noise is the brightest and darkest pixels in the image, we propose a simpler version of our method. In addition to the proposed method, we suggest a combination of the traditional median filter method with our method to yield better results when the percentage of the corrupted samples is high.

Image de-noising is a vital concern in image processing. Out of different available method wavelet thresolding method is one of the important approaches for image de-noises. In this paper we propose an adaptive method of image de-noising in the wavelet sub-band domain assuming the images to be contaminated with noise based on threshold estimation for each sub-band. Under this framework the proposed technique estimates the threshold level by apply sub-band of each decomposition level. This paper entails the development of a new MATLAB function based on our algorithm. The experimental evaluation of our proposition reveals that our method removes noise more effectively than the in-built function provided by MATLAB .One of its applications for Fingerprint de-noise due to importance of fingerprint for day-to-day life especially in computer security purposes. Fingerprint acts as a vital role for user authentication as it is unique and not duplicated. Unfortunately allusion Fingerprints may get corrupted and polluted with noise during possession, transmission or retrieval from storage media. Many image processing algorithms such as pattern recognition need a clean fingerprint image to work effectively which in turn needs effective ways of de-noising such images. We apply our proposed algorithm and compare other traditional algorithms for different noises.

This paper proposes an efficient and fast approach for filtering the salt and pepper and random valued impulse noise. The algorithm first implies the simple statistical operations for the identification of noisy pixel then after proposes the noisy pixel by an adaptive median filter based approach. The simple architecture and the utilization of computationally efficient mathematical operations for the processing makes the algorithm very fast. The proposed algorithm works well for low density as well as very high density salt and pepper noise and it also filters the random valued impulse noise efficiently. Finally the simulation results of denoising the images corrupted by salt and pepper and random valued impulse noise with the noise density up to 99 % has been presented. The results of proposed method are also compared with the other existing method, and the comparison shows the performance improvement of our method by acceptable margin.

This paper presents an adaptive fuzzy filter combined with median filter (AFM) to the reduction of intensive salt and pepper noise in gray level images. Firstly, noisy pixels are detected using spatial neighborhood and variance of the pixel values. Then, noisy pixels are mainly removed with adaptive fuzzy filter, and then median filter is used for removing rest of the noisy pixels. The proposed method is able to reduce intensive salt and pepper noise in gray level digital images while preserving image details, and textures very well.

ABSTRACT Image enhancement and restoration in a noisy environment are fundamental problems in image processing. Various filtering techniques have been developed to suppress noise in order to improve the quality of images. Among diverse de-noising techniques, median filter is a well-known filter to deal with impulse noise in digitals images. However, due to some limitations associated with the standard median filtering approach, several new improved versions of the median filtering method have been proposed by researchers. In this study, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) was presented for restoring digital images corrupted by salt and pepper noise by a dynamic median filter that will adapt itself to the local noise intensity. Simulation results indicate that the proposed approach shows a high-quality restoration of filtered images than those using static median filter or others filters, in terms of peak signal-to-noise ratio (PSNR).

ABSTRACT With the advantages of information technology, many techniques have been discovered to represent data. A large amount of data is commonly represented by an image. "Accuracy of this data representation" by an image is another important task in image processing. "Representation of data via a compound image" is becoming most popular day by day. A compound image contains text, graphics and picture to represent data in the form of image. This compound image may be corrupted during acquisition, compression and transmission of data. Different restoration techniques have been being proposed to reduce noise from image. In this paper; we proposed variable filtering window size to detect salt-and -pepper noise from a corrupted compound image and then replace this corrupted pixel with the median value of the filtering window.

In this study, we propose a new method, i.e. Adaptive Riesz Mean Filter (ARmF), by operationalizing pixel similarity for salt-and-pepper noise (SPN) removal. Afterwards, we compare the results of ARmF, A New AdaptiveWeighted Mean Filter (AWMF), Different Applied Median Filter (DAMF), Noise Adaptive Fuzzy Switching Median Filter (NAFSMF), Based on Pixel Density Filter (BPDF), Modified Decision-Based Unsymmetric Trimmed Median Filter (MDBUTMF) and Decision-Based Algorithm (DBA) by using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), Image Enhancement Factor (IEF), and Visual Information Fidelity (VIF) for 20 traditional test images (Lena, Cameraman, Barbara, Baboon, Peppers, Living Room, Lake, Plane, Hill, Pirate, Boat, House, Bridge, Elaine, Flintstones, Flower, Parrot, Dark- HairedWoman, BlondeWoman, and Einstein), 40 test images in the TESTIMAGES Database, and 200 RGB test images from the UC Berkeley Dataset ranging in noise density from 10% to 90%. Moreover, we compare the running time of these algorithms. These results show that ARmF outperforms the methods mentioned above. We finally discuss the need for further research.

Quality is a very important parameter for all objects and their functionalities. For authentic quality evaluation ground truth is required. But in practice, it is very difficult to find the ground truth. Usually, image quality is being assessed by MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio). In contrast to MSE and PSNR, recently, SSIM (Structured Similarity Indexing Method) is proposed which compares the structural measure between obtained and original images. This paper is mainly stressed on SSIM and compares the finding with MSE and PSNR. To measure the image quality we have done a simulation work by adding noise to bench-marked original images and then calculated MSE, PSNR and SSIM of the corresponding image. We have found the superiority of the SSIM in comparison to MSE and PSNR.

At the present time, the aptitude in contemplation of accessing as well as sharing images has become progressively facile with the Internet allowing people to procure information remotely from anywhere in the entire world. Moreover, there has been also an expansion with regard to the number of the still digital images over the internet for the sake of the fact that a vast number of millions of people are capturing digital photos. Fundamentally, the procedure of digital watermarking can be delineated as a method for embedding information into another signal (a digital signal). In case of digital images, the embedded information can be either visible or hidden from the user. In this project, we will concentrate on imperceptible watermarks. The principal intention of digital watermarks is to provide copyright protection for intellectual property that is in digital format. Typical usage scenarios for watermarking are such as copyright protection and data authentication. In this paper, we describe an imperceptible and a robust DWT digital Image Watermarking algorithm.The algorithm watermarks a given digital image using a Discrete Wavelet Transform.