Dibyendu Ghoshal

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

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

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.

Domain-specific augmentations for High-Level Synthesis

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.

A study on edge marking scheme of various standard edge detectors

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

Effect of Various Spatial Sharpening Filters on the Performance of the Segmented Images using Watershed Approach based on Image Gradient Magnitude and Direction

various spectrum of image processing, images are acquired with low variations in the intensity level and thus they possess small gradient values. In these cases, it is convenient to apply watershed segmentation on the gradient image, rather than the original image. The most common output of these segmented images is over segmentation and it implies the presence of numerous watershed ridges that do not correspond to the object boundaries of interest. Under this intermingled problematic scenario, the role of the spatial edge sharpening filters should not be ignored. This research paper deals with the role of various edge sharpening filters and to find the ultimate effect of them on the output image using watershed algorithm is presented.

A Modified Watershed Segmentation Algorithm using Distances Transform for Image Segmentation

In this paper, we propose a modified watershed algorithm for image segmentation using distances transform and image smoothing method, an improved version of watershed segmentation. This algorithm allows better boundary localization due to the edge information brought by watersheds. Thus, the proposed method has been found to be able to reduce over segmentation and this would ultimately lead to easier handling by the machine towards higher level of processing at subsequent stages. The algorithm has been tested on colored image obtained from real life and has been found to yield satisfactory segmentation results.

Watershed Segmentation based on Distance Transform and Edge Detection Techniques

An edge detection algorithm for digital images is proposed in this paper. Edge detection is one of the important and most difficult tasks in image processing and analysis. In images edges can create major variation in the picture quality where edges are areas with strong intensity contrasts. Edges in digital images are areas with strong intensity contrasts and a jump in intensity from one pixel to the next can create major variation in the picture quality. This paper proposed an effective edge detection algorithm based morphological edge detectors and watershed segmentation algorithm using distance transform. The result confirms that the proposed algorithm is found to yield satisfactory and efficient segmentation of the digital images for edge detection. Experimental result presented in this paper is obtained by using MATLAB.

An improved method for the enhancement of under ocean image

Image Enhancement is a process of improving the quality of an image by improving the contrast of the images. Images acquired underwater usually suffers from non-uniform illumination, low-contrast, motion blur effect due to turbulence in the flow of water, in under ocean environment, scattering of light from different particles of various sizes, diminished intensity and color level due to poor visibility conditions, suspended moving particles and so on. Due to all these factors, various types of noises occur and to reduce the effects arising out of these factors, a number of methods are required to be incorporated to improve the quality of underwater images. The present paper shows a comparative study of the various image enhancement techniques used for enhancing underwater images and introduces a suitable novel hybrid method for improving the image quality.

Extraction of time invariant lips based on Morphological Operation and Corner Detection Method

The study presents a robust yet simple technique for the extraction of lips from human face. Harris corner detection is applied on a morphologically pre-processed human face and the location and dimension of the lips along the horizontal and vertical directions are obtained. The lip dimension thus found is compared with a predetermined dimension of the same obtained through ‘data cursor’ operator prevailing in Matlab toolbox. The dimension obtained from the present technique is accepted only when they fall within a certain meaningful latter. The proposed technique is found to yield 88% of average correct rate of lip extraction from human faces of ORL Face Dataset. General Terms Image processing, Harris corner detector

A study on some aspects of reconstruction of images by parallel beam back projection method

Parallel beam back projection method has been used to reconstruct the images collected from standard books or literature which had been acquired by using energy source of various frequency bands in electromagnetic spectrum. The present study has included various images in positive as well as negative forms. Images of stellar objects, natural phenomena and normal objects has been analysed by varying the image resolution and entropy of the reconstructed images have been calculated for various number of projections. It has been observed that the number of projections becomes higher with the increase of image resolution for having the larger amount of entropy. The study has also indicated that the entropy achieved for the reconstructed image at first increases and then passes through a maximum followed by a decreasing nature with the increase of number of projections. Histogram equalisation studies have further been made on each reconstructed image to have an insight to the contrast of reconstructed images.