Jagroop Singh

Image denoising using spatial domain filters: A quantitative study

Image denoising is the first preprocessing step dealing with image processing. In image denoising an image is processed using certain restoration techniques to remove induced noise which may creep in the image during acquisition, transmission or compression process. Examples of noise in an image can be Additive White Gaussian Noise (AWGN), Impulse Noise, etc. The goal of restoration techniques is to obtain an image that is as close to the original input image as possible. In this paper objective evaluation methods are used to judge the efficiency of different types of spatial domain filters applied to different noise models, with a quantitative approach. Performance of each filter is compared as they are applied on images affected by a wide variety of noise models. Conclusions are drawn in the end, about which filter is best suited for a number of noise models individually induced in an image, according to the experimental data obtained.

Detection method and filters for blocking effect reduction of highly compressed images

Image compression plays a pivotal role in minimizing the data size and reduction in transmission costs. Many coding techniques have been developed, but the most effective is the JPEG compression. However, the reconstructed images from JPEG compression produce noticeable image degradations near block boundaries called blocking artifacts, particularly in highly compressed images. A method to detect and reduce these artifacts without smoothing images and without removing perceptual features has been presented in this paper. In this work, a low computational deblocking filter with four modes is proposed, including three frequency-related modes (smooth, non-smooth, and intermediate) and a corner mode for the corner of four blocks. Extensive experiments and comparison with other deblocking methods have been conducted on the basis of PSNR, MSSIM, SF, and MOS to justify the effectiveness of the proposed method. The proposed algorithm keeps the computation lower and achieves better detail preservation and artifact removal performance.

Detection methods for blocking artefacts in transform coded images

In block discrete cosine transform-based image compression, the blocking artefacts are the main cause of degradation, especially at higher compression ratio. It is of interest to be able to numerically assess the degree of blocking artefacts as it plays an important role in the design, optimisation and assessment of image and video coding systems. In this work, comparison of novel algorithms based on different modelling functions for blocking artefact detection in compressed images is proposed. The authors’ experiment results show that for all types of images, the proposed methods detect blocking artefacts more accurately as compared with other post-processing methods/techniques and is very efficient and stable since the signal need not be compressed/decompressed.

Blocking artefact detection in block-based DCT compressed images

The perceived quality of images reconstructed from low bit rate compression is severely degraded by appearance of transform coding blocking artefacts. It is of interest to be able to numerically assess the degree of blocking artefacts as it plays an important role in the design, optimisation and assessment of image and video coding systems. The proposed algorithm is based on new modelling function for blocking artefact detection in compressed images. Our experiment results show that the proposed method exhibits satisfactory performance as compared to other post-processing method’s/techniques and is very efficient and stable since the signal need not be compressed/decompressed.

Improved HRV characterization using OCDWT

In this paper an over-complete discrete wavelet transform (OCDWT) algorithm, obtained by blending two wavelet transform implementations that is redundant wavelet transform and the Mallats multiresolution decomposition, has been proposed to retrieve the time-varying characteristics of HRV under two different postures, supine and standing. The OCDWT algorithm is critically sub-sampled to a given level of decomposition, below which it is then fully sampled. Five subjects were included to investigate posture-related HRV. The results showed that the high frequency fluctuations are larger in supine and get significantly reduced in standing in comparison to low frequency variations. Moreover, the very low frequency heart beat fluctuations during supine were greater than during standing. Further a comparative analysis has also been made between the Mallats and OCDWT implementation in order to show the superiority of proposed algorithm.