Combined wavelet domain and temporal video denoising

Pižurica, Aleksandra; Zlokolica, Vladimir; Philips, Wilfried

doi:10.1109/avss.2003.1217940

Cited by 45 publications

(61 citation statements)

References 20 publications

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“…The tests were run on a set of four different image sequences-"Football," "Tennis," "Flower Garden," and "Mobile." Each of the four test sets is superimposed with synthetic white Gaussian noise, using noise levels 5,10,15,20,25,30,35,40, and 50. The translation between noise level and mean PSNR of the noisy sequences appears in Table III, as the clipping of out-of-range gray-values causes some variation, especially noticed in the strong noise cases.…”

Section: B Comparison Resultsmentioning

confidence: 99%

“…Spatial filtering may also be used, with stronger emphasis in areas in which the motion estimation is not as reliable. A similar approach described in [10] detects for each pixel weather it has undergone motion or not. Spatial filtering is applied to each image, and for each pixel with no motion detected, the results of the spatial filtering are recursively averaged with results from previous frames.…”

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confidence: 99%

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Image Sequence Denoising via Sparse and Redundant Representations

Protter

Elad

2009

IEEE Trans. on Image Process.

294

166

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Abstract-In this paper, we consider denoising of image sequences that are corrupted by zero-mean additive white Gaussian noise. Relative to single image denoising techniques, denoising of sequences aims to also utilize the temporal dimension. This assists in getting both faster algorithms and better output quality. This paper focuses on utilizing sparse and redundant representations for image sequence denoising, extending the work reported in [1], [2]. In the single image setting, the K-SVD algorithm is used to train a sparsifying dictionary for the corrupted image. This paper generalizes the above algorithm by offering several extensions: i) the atoms used are 3-D; ii) the dictionary is propagated from one frame to the next, reducing the number of required iterations; and iii) averaging is done on patches in both spatial and temporal neighboring locations. These modifications lead to substantial benefits in complexity and denoising performance, compared to simply running the single image algorithm sequentially. The algorithm's performance is experimentally compared to several state-of-the-art algorithms, demonstrating comparable or favorable results.

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Section: B Comparison Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Image Sequence Denoising via Sparse and Redundant Representations

Protter

Elad

2009

IEEE Trans. on Image Process.

294

166

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“…In order to increase the 3D filtering performance of the method proposed in [8] and Video denoising uses 2D and 3D dual-tree complex wavelet transforms. [9] Shows 2D wavelet based filtering and temporal mean filtering that uses pixel based motion detection.…”

Section: Video Denoising -Related Workmentioning

confidence: 99%

Noise Reduction in Video Sequences The State of Art and the Technique for Motion Detection

Reeja¹,

Kavya²

2012

IJCA

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This paper provides a detailed state of the art of different video denoising techniques. Most of the video denoising algorithms are done through the motion detection technique. The main goal is to give a survey of various noise reduction techniques for video. Object detection is the first level of video denoising. The first level can be achieved through Motion Detection. This paper explained about the motion estimation and compensation techniques. The different video denoising techniques, motion detection techniques and the noises used are shown through the taxonomy. General TermsImage processing, motion detection KeywordsObject tracking, frame differencing, image and video denoising.

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“…The above settlement is quite common to many temporal-filtering-based video denoising algorithms [17,37], with various modifications encountered ccording to the involved motion detection/estimation parameters. The noise estimations are also refined following the outcome of (12) and the J N∼4 (w i , w j , n) components are extracted similarly to the J N∼1 and J N∼3 matrices (10), (11).…”

Section: Video Denoising By Means Of Spatiotemporal Wavelet Filteringmentioning

confidence: 99%

Joint Wavelet Video Denoising and Motion Activity Detection in Multimodal Human Activity Analysis: Application to Video-Assisted Bioacoustic/Psychophysiological Monitoring

Dimoulas

Avdelidis

Kalliris

et al. 2007

EURASIP J. Adv. Signal Process.

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The current work focuses on the design and implementation of an indoor surveillance application for long-term automated analysis of human activity, in a video-assisted biomedical monitoring system. Video processing is necessary to overcome noise-related problems, caused by suboptimal video capturing conditions, due to poor lighting or even complete darkness during overnight recordings. Modified wavelet-domain spatiotemporal Wiener filtering and motion-detection algorithms are employed to facilitate video enhancement, motion-activity-based indexing and summarization. Structural aspects for validation of the motion detection results are also used. The proposed system has been already deployed in monitoring of long-term abdominal sounds, for surveillance automation, motion-artefacts detection and connection with other psychophysiological parameters. However, it can be used to any video-assisted biomedical monitoring or other surveillance application with similar demands.

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Combined wavelet domain and temporal video denoising

Cited by 45 publications

References 20 publications

Image Sequence Denoising via Sparse and Redundant Representations

Image Sequence Denoising via Sparse and Redundant Representations

Noise Reduction in Video Sequences The State of Art and the Technique for Motion Detection

Joint Wavelet Video Denoising and Motion Activity Detection in Multimodal Human Activity Analysis: Application to Video-Assisted Bioacoustic/Psychophysiological Monitoring

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