88 research outputs found
High-ISO long-exposure image denoising based on quantitative blob characterization
Blob detection and image denoising are fundamental, sometimes related tasks in computer vision. In this paper, we present a computational method to quantitatively measure blob characteristics using normalized unilateral second-order Gaussian kernels. This method suppresses non-blob structures while yielding a quantitative measurement of the position, prominence and scale of blobs, which can facilitate the tasks of blob reconstruction and blob reduction. Subsequently, we propose a denoising scheme to address high-ISO long-exposure noise, which sometimes spatially shows a blob appearance, employing a blob reduction procedure as a cheap preprocessing for conventional denoising methods. We apply the proposed denoising methods to real-world noisy images as well as standard images that are corrupted by real noise. The experimental results demonstrate the superiority of the proposed methods over state-of-the-art denoising methods
NLH: A Blind Pixel-level Non-local Method for Real-world Image Denoising
Non-local self similarity (NSS) is a powerful prior of natural images for
image denoising. Most of existing denoising methods employ similar patches,
which is a patch-level NSS prior. In this paper, we take one step forward by
introducing a pixel-level NSS prior, i.e., searching similar pixels across a
non-local region. This is motivated by the fact that finding closely similar
pixels is more feasible than similar patches in natural images, which can be
used to enhance image denoising performance. With the introduced pixel-level
NSS prior, we propose an accurate noise level estimation method, and then
develop a blind image denoising method based on the lifting Haar transform and
Wiener filtering techniques. Experiments on benchmark datasets demonstrate
that, the proposed method achieves much better performance than previous
non-deep methods, and is still competitive with existing state-of-the-art deep
learning based methods on real-world image denoising. The code is publicly
available at https://github.com/njusthyk1972/NLH.Comment: 14 pages, 9 figures, 10 tables, accept by IEEE TI
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