Proceedings ArticleDOI
Motion-aware noise filtering for deblurring of noisy and blurry images
Yu-Wing Tai,Stephen Lin +1 more
- pp 17-24
Reads0
Chats0
TLDR
This method takes advantage of estimated motion blur kernels to improve denoising, by constraining the denoised image to be consistent with the estimated camera motion, which leads to higher quality blur kernel estimation and deblurring performance.Abstract:
Image noise can present a serious problem in motion deblurring. While most state-of-the-art motion deblurring algorithms can deal with small levels of noise, in many cases such as low-light imaging, the noise is large enough in the blurred image that it cannot be handled effectively by these algorithms. In this paper, we propose a technique for jointly denoising and deblurring such images that elevates the performance of existing motion deblurring algorithms. Our method takes advantage of estimated motion blur kernels to improve denoising, by constraining the denoised image to be consistent with the estimated camera motion (i.e., no high frequency noise features that do not match the motion blur). This improved denoising then leads to higher quality blur kernel estimation and deblurring performance. The two operations are iterated in this manner to obtain results superior to suppressing noise effects through regularization in deblurring or by applying denoising as a preprocess. This is demonstrated in experiments both quantitatively and qualitatively using various image examples.read more
Citations
More filters
Journal ArticleDOI
From action to activity
TL;DR: Experiments demonstrated that the approach is able to recognize activities with high accuracy from temporal patterns, and that temporal patterns can be used effectively as a mid-level feature for activity representation.
Proceedings ArticleDOI
Dynamic Scene Deblurring
TL;DR: This paper proposes a novel energy model designed with the weighted sum of multiple blur data models, which estimates different motion blurs and their associated pixel-wise weights, and resulting sharp image and demonstrates that this method outperforms conventional approaches in deblurring both dynamic scenes and static scenes.
Journal ArticleDOI
Image Deblurring via Enhanced Low-Rank Prior
TL;DR: An enhanced prior for image deblurring is introduced by combining the low rank prior of similar patches from both the blurry image and its gradient map, and a weighted nuclear norm minimization method is employed to further enhance the effectiveness of low-rank prior.
Proceedings ArticleDOI
Handling Noise in Single Image Deblurring Using Directional Filters
TL;DR: This work proposes a new method for handling noise in blind image deconvolution based on new theoretical and practical insights, and observes that applying a directional low-pass filter to the input image greatly reduces the noise level, while preserving the blur information in the orthogonal direction to the filter.
Proceedings ArticleDOI
Discriminative Non-blind Deblurring
TL;DR: This work proposes a discriminative model cascade for non-blind deblurring, which consists of a Gaussian CRF at each stage, based on the recently introduced regression tree fields, and trains the model by loss minimization and uses synthetically generated blur kernels to generate training data.
References
More filters
Proceedings ArticleDOI
Bilateral filtering for gray and color images
Carlo Tomasi,Roberto Manduchi +1 more
TL;DR: In contrast with filters that operate on the three bands of a color image separately, a bilateral filter can enforce the perceptual metric underlying the CIE-Lab color space, and smooth colors and preserve edges in a way that is tuned to human perception.
Journal ArticleDOI
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
TL;DR: An algorithm based on an enhanced sparse representation in transform domain based on a specially developed collaborative Wiener filtering achieves state-of-the-art denoising performance in terms of both peak signal-to-noise ratio and subjective visual quality.
Proceedings ArticleDOI
A non-local algorithm for image denoising
TL;DR: A new measure, the method noise, is proposed, to evaluate and compare the performance of digital image denoising methods, and a new algorithm, the nonlocal means (NL-means), based on a nonlocal averaging of all pixels in the image is proposed.
Journal ArticleDOI
A Review of Image Denoising Algorithms, with a New One
TL;DR: A general mathematical and experimental methodology to compare and classify classical image denoising algorithms and a nonlocal means (NL-means) algorithm addressing the preservation of structure in a digital image are defined.
Journal ArticleDOI
Bayesian-Based Iterative Method of Image Restoration
TL;DR: An iterative method of restoring degraded images was developed by treating images, point spread functions, and degraded images as probability-frequency functions and by applying Bayes’s theorem.