This paper proposes a two-phase scheme for removing salt-and-pepper impulse noise. In the first phase, an adaptive median filter is used to identify pixels which are likely to be contaminated by noise (noise candidates). In the second phase, the image is restored using a specialized regularization method that applies only to those selected noise candidates. In terms of edge preservation and noise suppression, our restored images show a significant improvement compared to those restored by using just nonlinear filters or regularization methods only. Our scheme can remove salt-and-pepper-noise with a noise level as high as 90%.

/pdf/salt-and-pepper-noise-removal-by-median-type-noise-detectors-3g3v2kn635.pdf

Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization

LLNet: A deep autoencoder approach to natural low-light image enhancement

A new decision-based algorithm is proposed for restoration of images that are highly corrupted by impulse noise. The new algorithm shows significantly better image quality than a standard median filter (SMF), adaptive median filters (AMF), a threshold decomposition filter (TDF), cascade, and recursive nonlinear filters. The proposed method, unlike other nonlinear filters, removes only corrupted pixel by the median value or by its neighboring pixel value. As a result of this, the proposed method removes the noise effectively even at noise level as high as 90% and preserves the edges without any loss up to 80% of noise level. The proposed algorithm (PA) is tested on different images and is found to produce better results in terms of the qualitative and quantitative measures of the image

A New Fast and Efficient Decision-Based Algorithm for Removal of High-Density Impulse Noises

In surveillance, monitoring and tactical reconnaissance, gathering the right visual information from a dynamic environment and accurately processing such data are essential ingredients to making informed decisions which determines the success of an operation. Camera sensors are often cost-limited in ability to clearly capture objects without defects from images or videos taken in a poorly-lit environment. The goal in many applications is to enhance the brightness, contrast and reduce noise content of such images in an on-board real-time manner. We propose a deep autoencoder-based approach to identify signal features from low-light images handcrafting and adaptively brighten images without over-amplifying the lighter parts in images (i.e., without saturation of image pixels) in high dynamic range. We show that a variant of the recently proposed stacked-sparse denoising autoencoder can learn to adaptively enhance and denoise from synthetically darkened and noisy training examples. The network can then be successfully applied to naturally low-light environment and/or hardware degraded images. Results show significant credibility of deep learning based approaches both visually and by quantitative comparison with various popular enhancing, state-of-the-art denoising and hybrid enhancing-denoising techniques.

LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement

The known median-based denoising methods tend to work well for restoring the images corrupted by random-valued impulse noise with low noise level but poorly for highly corrupted images. This letter proposes a new impulse detector, which is based on the differences between the current pixel and its neighbors aligned with four main directions. Then, we combine it with the weighted median filter to get a new directional weighted median (DWM) filter. Extensive simulations show that the proposed filter not only can provide better performance of suppressing impulse with high noise level but can preserve more detail features, even thin lines. As extended to restoring corrupted color images, this filter also performs very well

A New Directional Weighted Median Filter for Removal of Random-Valued Impulse Noise

Recently, a two-phase scheme for removing salt-and-pepper impulse noise has been proposed [R.H. Chan et al], In the first phase, an adaptive median filter is used to identify pixels which are likely to be contaminated by noise (noise candidates). In the second phase, the image is restored by minimizing a specialized regularization functional that applies only to those selected noise candidates. As an extension of this work, we propose an efficient method to accomplish the second phase. The speed of our method can be double as that of the method proposed in [R.H. Chan et al] for images contaminated by 30% salt-and-pepper noise and is faster for higher noise level.

/pdf/minimization-of-detail-preserving-regularization-functional-2t607gw45s.pdf

Minimization of detail-preserving regularization functional by Newton's method with continuation

Recently, two-phase schemes for removing salt-and-pepper and random-valued impulse noise are proposed in [6, 7]. The first phase uses decision-based median filters to locate those pixels which are likely to be corrupted by noise (noise candidates). In the second phase, these noise candidates are restored using a detail-preserving regularization method which allows edges and noise-free pixels to be preserved. As shown in [18], this phase is equivalent to solving a one-dimensional nonlinear equation for each noise candidate. One can solve these equations by using Newton’s method. However, because of the edgepreserving term, the domain of convergence of Newton’s method will be very narrow. In this paper, we determine the initial guesses for these equations such that Newton’s method will always converge.

/pdf/convergence-of-newton-s-method-for-a-minimization-problem-in-13s3ycfhdv.pdf

Chung-Wa Ho

Papers

Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization

Minimization of detail-preserving regularization functional by Newton's method with continuation

Convergence of newton's method for a minimization problem in impulse noise removal ∗1)