Impulse noise removal using SVM classification based fuzzy filter from gray scale images

In this study, a combination of adaptive vector median filter (VMF) and weighted mean filter is proposed for removal of high-density impulse noise from colour images. In the proposed filtering scheme, the noisy and non-noisy pixels are classified based on the non-causal linear prediction error. For a noisy pixel, the adaptive VMF is processed over the pixel where the window size is adapted based on the availability of good pixels. Whereas, a non-noisy pixel is substituted with the weighted mean of the good pixels of the processing window. The experiments have been carried out on a large database for different classes of images, and the performance is measured in terms of peak signal-to-noise ratio, mean squared error, structural similarity and feature similarity index. It is observed from the experiments that the proposed filter outperforms (~1.5 to 6 dB improvement) some of the existing noise removal techniques not only at low density impulse noise but also at high-density impulse noise.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2016.0320

Combination of adaptive vector median filter and weighted mean filter for removal of high-density impulse noise from colour images

A new filter was created by improving the standard Kuwahara filter. It allows more efficient noise reduction without blurring the edges and image preparation for segmentation and further analyses operations. One of the biggest and most common restrictions encountered in filter algorithms is the need for a declarative definition of the filter window size or the number of iterations that an operation should be repeated. In the case of the proposed solution, we are dealing with automatic adaptation of the algorithm to the local environment of each pixel in the processed image.

/pdf/adaptive-kuwahara-filter-12ylti1w61.pdf

Adaptive Kuwahara filter

As today's computer architectures are becoming more and more heterogeneous, a plethora of options including CPUs, GPUs, DSPs, reconfigurable logic (FPGAs), and other application-specific processors come into consideration for close-to-sensor processing. Especially, in the domain of image processing on mobile devices, among numerous design challenges, a very stringent energy budget is of utmost importance, making embedded GPUs and FPGAs ideal targets for implementation. Recently, the HIPAcc framework was proposed as a means for automatic code generation of image processing algorithms for embedded GPUs, based on a Domain-Specific Language (DSL). Despite of huge advancements in High-Level Synthesis (HLS) for FPGAs, designers are still required to have detailed knowledge about coding techniques and the targeted architecture to achieve efficient solutions. As a remedy, in this work, we propose code generation techniques for C-based HLS from a common high-level DSL description targeting FPGAs. Our approach includes FPGA-specific memory architectures for handling point and local operators, numerous high-level transformations, and automatic test bench generation. We evaluate our approach by comparing the resulting hardware accelerators to existing frameworks in terms of performance and resource requirements. Moreover, we assess the achieved energy efficiency in contrast to software implementations, generated by HIPAcc from the same code base, executed on GPUs.

https://www12.informatik.uni-erlangen.de/publications/reiche/reiche2014codes.pdf

Code generation from a domain-specific language for C-based HLS of hardware accelerators

A new impulsive noise removal filter, adaptive dynamically weighted median filter (ADWMF), is proposed. A popular method for removing impulsive noise is a median filter whereas the weighted median filter and center weighted median filter were also investigated. ADWMF is based on weighted median filter. In ADWMF, instead of fixed weights, weightages of the filter are dynamically assigned with the results of noise detection. A simple and efficient noise detection method is also used to detect noise candidates and dynamically assign zero or small weights to the noise candidates in the window. This paper proposes an adaptive method which increases the window size according to the amounts of impulsive noise. Simulation results show that the AMWMF works better for both images with low and high density of impulsive noise than existing methods work.

/pdf/an-adaptive-dynamically-weighted-median-filter-for-impulse-1ktwo5j3gc.pdf

An adaptive dynamically weighted median filter for impulse noise removal

A new decision-based algorithm has been proposed for the restoration of digital images which are highly contaminated by the saturated impulse noise (i.e., salt-and-pepper noise). The proposed denoising algorithm performs filtering operation only to the corrupted pixels in the image, keeping uncorrupted pixels intact. The present study has used a coupled window scheme for the removal of high density noise. It has used sliding window of increasing dimension, centered at any pixel and replaced the noisy pixels consecutively by the median value of the window. However, if the entire pixels in the window are noisy, then the dimension of sliding window is increased in order to obtain the noise-free pixels for median calculation. Consequently, this algorithm has been found to be able to remove the high density salt-and-pepper noise and also preserved the fine details of the four images, Lena, Elaine, Rhythm, and Sunny, used as test images in this study (The latter two real-life images have been acquired using Sony: Steady Shot DSC- S3000). Experimentally, it has been found that the proposed algorithm yields better peak signal-to-noise ratio, image enhancement factor, structural similarity index measure and image quality index, compared with the other state-of-art median-based filters viz. standard median filter, adaptive median filter, progressive switched median filter, modified decision-based algorithm and modified decision-based unsymmetric trimmed median filter.

A new approach for high density saturated impulse noise removal using decision-based coupled window median filter

This paper presents a study on edge marking scheme of various standard edge detectors viz. Sobel, Prewitt, Roberts, Laplacian of Gaussian (LoG) and Canny. On the basis of edge map obtained for a synthetic rectangular board image, obtained from five edge detectors, suggests classifying the detectors in three categories namely, pre-marker, post-marker and mixed-marker. Pratt’s figure of merit (PFOM) is used as a quantitative evaluation criterion for the above mentioned classification. Experimental results obtained for Lena and Parrot image provides convincing results to establish proposed classification. General Terms Image Processing

/pdf/a-study-on-edge-marking-scheme-of-various-standard-edge-20bu499j8w.pdf

A study on edge marking scheme of various standard edge detectors

Existing impulse noise reduction techniques perform well at low noise densities; however, their performance drops sharply at higher noise densities. In this paper, we propose a two-stage scheme to surmount this problem. In the proposed approach, first stage consists of impulse detection unit followed by the filtering operation in the second stage. We have employed a genetic expression programming-based classifier for the detection of impulse noise-corrupted pixels. To reduce the blurring effect caused due to filtering operation on the noise-free pixels, we filter the detected noisy pixels only by using a modified median filter. Better peak signal-to-noise ratio, structural similarity index measure, and visual output imply the efficacy of the proposed scheme for noise reduction at higher noise densities.

A study on genetic expression programming-based approach for impulse noise reduction in images

The paper proposes a shot boundary detection system using Gist and local descriptor. Gist provides the perceptual and conceptual information of a scene. The proposed system can be roughly divided into three steps. The first step consists of forming of groups of frames by calculating the correlation of the Gist features between consecutive frames of the video. Secondly, abrupt transitions are found out using the group (G), MSER and a threshold (for abrupt separately, $$th_{cut}$$
 ). And lastly, gradual transitions of the video are found using triangular pattern matching. We have performed the experiment on TRECVid 2001 and 2007 dataset. The novel contribution of this paper is that the proposed system shows an activity-based shot boundary detection where only the possible transition regions of a video are considered for shot detection. This approach reduces the computational complexity by processing the transition regions only. We have achieved better results in terms of F1, precision and recall, when compared to previously published approaches.

Shot boundary detection using perceptual and semantic information

High-Level Synthesis (HLS) has become a very popular instrument to facilitate rapid development of production-ready implementations for FPGAs Ever increasing flexibility of the frameworks, however, demands a very high level of domain-specific knowledge from the designer Examples for such knowledge in window-based image processing are median computation and border handling Depending on the size of the considered window, writing the code to perform such operations may become overwhelming even at very high abstraction levels To increase productivity and to make the underlying architecture accessible to non-experts, we propose to combine HLS with domain-specific augmentations Specifically, we propose a new language extension in form of a reduction for sorting and median computation Furthermore, we introduce a new high-level transformation to perform multiple kinds of border treatment automatically Both augmentations may reduce the required amount of code lines considerably The increase in productivity is analyzed by comparing the lines of code necessary to specify a median filter for HLS in PAULA for synthesis using PARO and in C++ for synthesis using a commercial HLS tool

/pdf/domain-specific-augmentations-for-high-level-synthesis-1yrr2swugx.pdf

Vivek Singh Bhadouria

Papers

A new approach for high density saturated impulse noise removal using decision-based coupled window median filter

A study on edge marking scheme of various standard edge detectors

A study on genetic expression programming-based approach for impulse noise reduction in images

Shot boundary detection using perceptual and semantic information

Domain-specific augmentations for High-Level Synthesis