Turning diffusion-based image colorization into efficient color compression
The work of Levin et al.(2004) popularized stroke-based methods that add color to gray
value images according to a small amount of user-specified color samples. Even though …
value images according to a small amount of user-specified color samples. Even though …
Deep spatial and tonal data optimisation for homogeneous diffusion inpainting
Diffusion-based inpainting can reconstruct missing image areas with high quality from
sparse data, provided that their location and their values are well optimised. This is …
sparse data, provided that their location and their values are well optimised. This is …
Evaluating the true potential of diffusion-based inpainting in a compression context
Partial differential equations (PDEs) are able to reconstruct images accurately from a small
fraction of their image points. The inpainting capabilities of sophisticated anisotropic PDEs …
fraction of their image points. The inpainting capabilities of sophisticated anisotropic PDEs …
Scalable coding of depth maps with RD optimized embedding
Recent work on depth map compression has revealed the importance of incorporating a
description of discontinuity boundary geometry into the compression scheme. We propose a …
description of discontinuity boundary geometry into the compression scheme. We propose a …
Compression of depth maps with segment-based homogeneous diffusion
The efficient compression of depth maps is becoming more and more important. We present
a novel codec specifically suited for this task. In the encoding step we segment the image …
a novel codec specifically suited for this task. In the encoding step we segment the image …
Gaining Insights into Denoising by Inpainting
The filling-in effect of diffusion processes is a powerful tool for various image analysis tasks
such as inpainting-based compression and dense optic flow computation. For noisy data, an …
such as inpainting-based compression and dense optic flow computation. For noisy data, an …
Optimising spatial and tonal data for PDE-based inpainting
Some recent methods for lossy signal and image compression store only a few selected
pixels and fill in the missing structures by inpainting with a partial differential equation (PDE) …
pixels and fill in the missing structures by inpainting with a partial differential equation (PDE) …
Combining image space and q-space PDEs for lossless compression of diffusion MR images
Diffusion MRI is a modern neuroimaging modality with a unique ability to acquire
microstructural information by measuring water self-diffusion at the voxel level. However, it …
microstructural information by measuring water self-diffusion at the voxel level. However, it …
Beyond pure quality: Progressive modes, region of interest coding, and real time video decoding for PDE-based image compression
Compared to transform-based image compression methods such as JPEG2000,
approaches based on partial-differential equations (PDEs) are in a proof-of-concept stage …
approaches based on partial-differential equations (PDEs) are in a proof-of-concept stage …
Compressing piecewise smooth images with the Mumford-Shah cartoon model
Compressing piecewise smooth images is important for many data types such as depth
maps in 3D videos or optic flow fields for motion compensation. Specialised codecs that rely …
maps in 3D videos or optic flow fields for motion compensation. Specialised codecs that rely …