Image Denoising Using Nonlocal Means with Shape-Adaptive Patches and New Weights

Digital images captured from CMOS/CCD image sensors are prone to noise due to inherent electronic fluctuations and low photon count. To efficiently reduce the noise in the image, a novel image denoising strategy is proposed, which exploits both nonlocal self-similarity and local shape adaptation. With wavelet thresholding, the residual image in method noise, derived from the initial estimate using nonlocal means (NLM), is exploited further. By incorporating the role of both the initial estimate and the residual image, spatially adaptive patch shapes are defined, and new weights are calculated, which thus results in better denoising performance for NLM. Experimental results demonstrate that our proposed method significantly outperforms original NLM and achieves competitive denoising performance compared with state-of-the-art denoising methods.

Wavelet-based Multi-focus Image Fusion using Average Method Noise Diffusion (AMND)

Aim: This paper presents a new and upgraded wavelet-based multi-focus image fusion technique using average method noise diffusion (AMND). Objective: Improved visual appearance of the final image, no blurring in the final fused image and clearly visible objects (fine edges). Methods: This method extends the standard wavelet-based image fusion technique on multi-focus images by incorporating the hybrid of method noise and anisotropic diffusion in it. This hybrid structure of method noise and anisotropic diffusion is implemented as the post-processing operation in the proposed method. Results: The proposed work shows excellent results in terms of visual appearance and edge preservation. The experimental results of the proposed method are compared with some of the traditional and non-traditional approaches where the proposed method shows comparatively better results. Conclusion: This paper depicts the robustness, effectiveness and adaptive nature of method noise in the field of image enhancement especially in the field of image fusion. The performance of the proposed method is analyzed qualitatively (good visual appearance) and quantitatively (entropy, spatial frequency, and standard deviation). This method has the capability to be incorporated in real-time applications like surveillance in the visual sensor network (VSN).

Impact of Method Noise on SAR Image Despeckling

This article introduces the concept, use and implementation of method noise in the field of synthetic aperture radar (SAR) image despeckling. Method noise has the capability to enhance the efficiency and performance of any despeckling algorithm. It is easy, efficient and enhanced way of improving the results. The difference between speckled image and despeckled image contains some residual image information which is due to the inefficiency of the denoising algorithm. This article will compare the results of some standard methods with and without the use of method noise and prove its efficiency and validity. It also shows its best use in different ways of denoising. The results will be compared on the basis of performance metrics like PSNR and SSIM. The concept of method noise is not restricted to only SAR images. It has vast usage and application. It can be used in any denoising procedure such as medical images, optical image etc. but this paper shows the experimental results only on the SAR images.

An Efficient Image Denoising Based on Weiner Filter and Neigh Sure Shrink

Weiner filter denoise the image using linear stochastic framework. It eliminates the noise by estimating optimal filter for noisy input image by minimizing the mean square error between the desired image and estimated image. The main drawback of this filter is the performance is reduced when the noise is random and unknown as it has fixed frequency response for all frequencies. The efficiency of this filter can be increased by incorporating method noise thresholding using NeighSure shrink. This paper presents a method which is a blend of Weiner filter and wavelet based NeighSure shrink thresholding. The results indicates that the proposed method is significantly superior than wavelet thresholding, Weiner filter and Gaussian filter with its method noise thresholding techniques in terms of visual quality, Peak Signal to noise ratio and image quality index.