Removal Of Blocking Artifacts From JPEG-Compressed Images Using An Adaptive Filtering Algorithm

The aim of this research was to develop an algorithm that will produce a considerable improvement in the quality of JPEG images, by removing blocking and ringing artifacts, irrespective of the level of compression present in the image. We review multiple published related works, and finally present a computationally efficient algorithm for reducing the blocky and Gibbs oscillation artifacts commonly present in JPEG compressed images. The algorithm alpha-blends a smoothed version of the image with the original image; however, the blending is controlled by a limit factor that considers the amount of compression present and any local edge information derived from the application of a Prewitt filter. In addition, the actual value of the blending coefficient (α) is derived from the local Mean Structural Similarity Index Measure (MSSIM) which is also adjusted by a factor that also considers the amount of compression present. We also present our results as well as the results for a variety of other papers whose authors used other post compression filtering methods

Removal Of Blocking Artifacts From JPEG-Compressed Images Using Neural Network

The goal of this research was to develop a neural network that will produce considerable improvement in the quality of JPEG compressed images, irrespective of compression level present in the images. In order to develop a computationally efficient algorithm for reducing blocky and Gibbs oscillation artifacts from JPEG compressed images, we integrated artificial intelligence to remove blocky and Gibbs oscillation artifacts. In this approach, alpha blend filter [7] was used to post process JPEG compressed images to reduce noise and artifacts without losing image details. Here alpha blending was controlled by a limit factor that considers the amount of compression present, and any local information derived from Prewitt filter application in the input JPEG image. The outcome of modified alpha blend was improved by a trained neural network and compared with various other published works [7][9][11][14][20][23][30][32][33][35][37] where authors used post compression filtering methods

A hybrid face recognition technique as an anti-theft mechanism

This paper proposes an anti-theft mechanism uses biometric Face Recognition to identify thief along with alarming. This can be used as security for ATMs, airport's systems, medical records, identify customers, preventing fraud and providing VIP services as well as recognizing individuals with known shoplifting convictions and video surveillance. The proposed system based on Viola Jones algorithm, Wavelet transform and Principal Component Analysis. Experimental results are given to demonstrate the viability of the proposed face recognition system with achieved efficiency is 82%

Video Filtering Using Separable Four-Dimensional Nonlocal Spatiotemporal Transforms

The large number of practical application involving digital videos has motivated a significant interest in restoration or enhancement solutions to improve the visual quality under the presence of noise. We propose a powerful video denoising algorithm that exploits temporal and spatial redundancy characterizing natural video sequences to reduce the effects of noise. The algorithm implements the paradigm of nonlocal grouping and collaborative filtering, where a four-dimensional transform- domain representation is leveraged to enforce sparsity and thus regularize the data. Moreover we present an extension of our algorithm that can be effectively used as a deblocking and deringing filter to reduce the artifacts introduced by most of the popular video compression techniques. Our algorithm, termed V-BM4D, at first constructs three-dimensional volumes, by tracking blocks along trajectories defined by the motion vectors, and then groups together mutually similar volumes by stacking them along an additional fourth dimension. Each group is transformed through a decorrelating four-dimensional separable transform, and then it is collaboratively filtered by coeffcients shrinkage. The effectiveness of shrinkage is due to the sparse representation of the transformed group. Sparsity is achieved because of different type of correlation among the groups: local correlation along the two dimensions of the blocks, temporal correlation along the motion trajectories, and nonlocal spatial correlation along the fourth dimension. As a conclusive step, the different estimates of the filtered groups are adaptively aggregated and subsequently returned to their original position, to produce a final estimate of the original video. The proposed filtering procedure leads to excellent results in both objective and subjective visual quality, since in the restored video sequences the effect of the noise or of the compression artifacts is noticeably reduced, while the significant features are preserved. As demonstrated by experimental results, V-BM4D outperforms the state of the art in video denoising. /Kir1