Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast bilateral filtering for the display of high-dynamic-range images
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
ACM SIGGRAPH 2006 Papers
Multiscale shape and detail enhancement from multi-light image collections
ACM SIGGRAPH 2007 papers
Real-time edge-aware image processing with the bilateral grid
ACM SIGGRAPH 2007 papers
Edge-preserving decompositions for multi-scale tone and detail manipulation
ACM SIGGRAPH 2008 papers
Edge-preserving multiscale image decomposition based on local extrema
ACM SIGGRAPH Asia 2009 papers
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
SIAM Journal on Imaging Sciences
Globally Optimized Linear Windowed Tone Mapping
IEEE Transactions on Visualization and Computer Graphics
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Two-phase kernel estimation for robust motion deblurring
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Domain transform for edge-aware image and video processing
ACM SIGGRAPH 2011 papers
Image smoothing via L0 gradient minimization
Proceedings of the 2011 SIGGRAPH Asia Conference
High dynamic range image rendering with a retinex-based adaptive filter
IEEE Transactions on Image Processing
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We present an image decomposition method using L1 fidelity term with L0 norm of gradient to decompose an image into base layer and detail layer. Generally, the L1 fidelity should be preferable to the L2 norm when the erroneous measurements exist. It is also reported that the L0 norm of gradient is a better prior term than total variation and the L2 norm of gradient. Therefore, we combine these two benefits to obtain our base layer by adopting our method using L1 fidelity and L0 gradient. Our image decomposition method can be regarded as the fundamental tool to generate multiple image editing applications, such as image denoising, edge detection, detail enhancement, cartoon JPEG artifact removal, local tone mapping, and contrast enhancement under low backlight condition. Experimental results show that our proposed method is promising as compared to the existing methods.