Image Denoising Using Weighted Averaging

Zhou, Dengwen; Xiaoliu, Shen

doi:10.1109/cmc.2009.64

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…More recently, the DCT-based denoising approaches [16,27] use weights chosen inversely proportional to the number of non-zero coefficients of the DCT after thresholding, giving more weights to patches that have a lot of coefficients set to 0 (flat patches for example). Other approaches draw on similar ideas to derive optimal weights [9,32,20,14]. Instead of the variance, some authors also attempt to minimize the risk of the final estimator at each pixel, by making use of Stein's Unbiased Risk Estimator (SURE) [8,36].…”

Section: Introductionmentioning

confidence: 99%

A Unified View on Patch Aggregation

2019

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Patch-based methods are widely used in various topics of image processing, such as image restoration or image editing and synthesis. Patches capture local image geometry and structure and are much easier to model than whole images: in practice, patches are small enough to be represented by simple multivariate priors. An important question arising in all patch-based methods is the one of patch aggregation. For instance, in image restoration, restored patches are usually not compatible, in the sense that two overlapping restored patches do not necessarily yield the same values to their common pixels. A standard way to overcome this difficulty is to see the values provided by different patches at a given pixel as independent estimators of a true unknown value and to aggregate these estimators. This aggregation step usually boils down to a simple average, with uniform weights or with weights depending on the trust we have on these different estimators. In this paper, we propose a probabilistic framework aiming at a better understanding of this crucial and often neglected step. The key idea is to see the aggregation of two patches as a fusion between their models rather than a fusion of estimators. The proposed fusion operation is pretty intuitive and generalizes previous aggregation methods. It also yields a novel interpretation of the Expected Patch Log Likelihood (EPLL) proposed in [40].

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Section: Introductionmentioning

confidence: 99%

A Unified View on Patch Aggregation

2019

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“…The salt and pepper type noise occurs when the picture elements in the camera sensors do not function well or error in the memory location or during digitization process. A non linear scheme iscalled median filtering with success in this situation [4,17,18].…”

Section: Introductionmentioning

confidence: 99%

A new adaptive median filtering technique for removal of impulse noise from images

Patel¹,

Tripathi

Majhi

et al. 2011

Proceedings of the 2011 International Conference on Communication, Computing &Amp; Security - ICCCS '11

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This paper describes a new technique for the design of Adaptive Median Filtering Technique, aimed at removing the impulse noise (salt and pepper noise) from the image and reducing distortion in the image. The filter is designed to reconfigure itself and provide real-time noise reduction. This paper investigates a high-speed non-linear Adaptive median filter implementation. Then Adaptive Median Filter serves the dual purpose of removing the impulse noise from the image and reducing distortion in the image. Adaptive Median Filtering can achieve the filtering operation of an image corrupted with impulse noise up to 70%.

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“…Salt-and-pepper noise occurs when there is a fault or malfunction in the equipment capturing the picture or an error in the digitization process of the image or an error in storing the image to memory location. The performance of linear filters on Salt-and-pepper noise is unsatisfactory hence it cannot be used frequently [7,8,9].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Non-linear Filtering Techniques based on Quantitative Metrics using Different Images

Sujatha¹,

Vijayarajan²,

Gupta³

et al. 2012

IJCA

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Image filter is the process of removing various types of noise from the images. The ensuant image is an information rich image than the original input image. The filtering finds its application in many fields from medical imagery, face detection, robot navigation, object detection, aircraft maintenance to image enhancement and image restoration. In the field of medical sciences the filter serves the purpose of image enhancement for efficient disease diagnosis, in aircraft maintenance for the purpose of detection of faults during takeoff, in case of face detection, object recognition and robot navigation used for object detection. This paper uses different quantitative metrics to analyze the result of different filtering techniques on an image. Initially, well known registered images from various aspects of science and nature are taken such as one image ct.jpg from medical sciences, two images Lighthouse.jpg, Penguins.jpg of natural scenery, two images of faces Koala.jpg, lena.jpg and a picture of naturally grown flowers Tulips.jpg are taken as input. Filtering techniques namely Median Filter (MF), Adaptive Filter (AF), New Adaptive Median Filer (NAMF), New Adaptive Spatial Filter (NASF), Edge Preserving Smooth Filter (EPSF) are applied on them. Further the filtered images are analyzed using five quantitative metrics such as Entropy (EN), Standard Deviation (SD), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and Mean Absolute Error (MAE). From the experimental result and the corresponding metrics used we observed that the resultant image is more informative than the original source images.

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Image Denoising Using Weighted Averaging

Cited by 5 publications

References 3 publications

A Unified View on Patch Aggregation

A Unified View on Patch Aggregation

A new adaptive median filtering technique for removal of impulse noise from images

Analysis of Non-linear Filtering Techniques based on Quantitative Metrics using Different Images

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