Topic
Sharpening
About: Sharpening is a research topic. Over the lifetime, 5554 publications have been published within this topic receiving 43831 citations. The topic is also known as: Sharpen.
Papers published on a yearly basis
1 / 3
Papers
TL;DR: In contrast to acquisition-based noise reduction methods a postprocess based on anisotropic diffusion is proposed, which overcomes the major drawbacks of conventional filter methods, namely the blurring of object boundaries and the suppression of fine structural details.
Abstract: In contrast to acquisition-based noise reduction methods a postprocess based on anisotropic diffusion is proposed. Extensions of this technique support 3-D and multiecho magnetic resonance imaging (MRI), incorporating higher spatial and spectral dimensions. The procedure overcomes the major drawbacks of conventional filter methods, namely the blurring of object boundaries and the suppression of fine structural details. The simplicity of the filter algorithm permits an efficient implementation, even on small workstations. The efficient noise reduction and sharpening of object boundaries are demonstrated by applying this image processing technique to 2-D and 3-D spin echo and gradient echo MR data. The potential advantages for MRI, diagnosis, and computerized analysis are discussed in detail. >
1,320 citations
12 Dec 2011
TL;DR: This work presents a new image editing method, particularly effective for sharpening major edges by increasing the steepness of transition while eliminating a manageable degree of low-amplitude structures in an optimization framework making use of L0 gradient minimization.
Abstract: We present a new image editing method, particularly effective for sharpening major edges by increasing the steepness of transition while eliminating a manageable degree of low-amplitude structures. The seemingly contradictive effect is achieved in an optimization framework making use of L0 gradient minimization, which can globally control how many non-zero gradients are resulted in to approximate prominent structure in a sparsity-control manner. Unlike other edge-preserving smoothing approaches, our method does not depend on local features, but instead globally locates important edges. It, as a fundamental tool, finds many applications and is particularly beneficial to edge extraction, clip-art JPEG artifact removal, and non-photorealistic effect generation.
1,293 citations
TL;DR: A new method for unsharp masking for contrast enhancement of images is presented that employs an adaptive filter that controls the contribution of the sharpening path in such a way that contrast enhancement occurs in high detail areas and little or no image sharpening occurs in smooth areas.
Abstract: This paper presents a new method for unsharp masking for contrast enhancement of images. The approach employs an adaptive filter that controls the contribution of the sharpening path in such a way that contrast enhancement occurs in high detail areas and little or no image sharpening occurs in smooth areas.
921 citations
TL;DR: A new image editing method, particularly effective for sharpening major edges by increasing the steepness of transition while eliminating a manageable degree of low-amplitude structures, is presented.
Abstract: We present a new image editing method, particularly effective for sharpening major edges by increasing the steepness of transition while eliminating a manageable degree of low-amplitude structures....
892 citations
1 Oct 2017
TL;DR: This work incorporates domain-specific knowledge to design the PanNet architecture by focusing on the two aims of the pan-sharpening problem: spectral and spatial preservation, and shows that the trained network generalizes well to images from different satellites without needing retraining.
Abstract: We propose a deep network architecture for the pan-sharpening problem called PanNet. We incorporate domain-specific knowledge to design our PanNet architecture by focusing on the two aims of the pan-sharpening problem: spectral and spatial preservation. For spectral preservation, we add up-sampled multispectral images to the network output, which directly propagates the spectral information to the reconstructed image. To preserve spatial structure, we train our network parameters in the high-pass filtering domain rather than the image domain. We show that the trained network generalizes well to images from different satellites without needing retraining. Experiments show significant improvement over state-of-the-art methods visually and in terms of standard quality metrics.
798 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 82 |
| 2024 | 154 |
| 2023 | 254 |
| 2022 | 376 |
| 2021 | 128 |
| 2020 | 199 |