AI Image Enhancement
If you want to use our tool upscale your images professionally we offer 2 ways to do so, as a SAAS solution as well as a standalone solution. The standalone solution comes plug and play on on a dedicated notebook. Both solutions will be updated with regular intervals.
Contact us for more details about these products via email@example.com
Blind face restoration (model 1)usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details. However, very low-quality inputs cannot offer accurate geometric prior while high-quality references are inaccessible, limiting the applicability in real-world scenarios.
The second model mainly contains two parts: (a) the off-line generation of multi-scale component dictionaries from large amounts of high-quality images, which have diverse poses and expressions. K-means is adopted to generate K clusters for each component (i.e., left/right eyes, nose and mouth) on different feature scales. (b) The restoration process and dictionary feature transfer (DFT) block that are utilized to provide the reference details in a progressive manner. Here, DFT-i block takes the Scale-i component dictionaries for reference in the same feature level.
Model 3 demo will be released soon. This model is not based on a GAN but is a so called "diffusion model". Over the past 20 years, the use of variational methods and nonlinear partial differential equations (PDEs) has significantly grown and evolved to address the image restoration problem. Here we consider image restoration as the classical inverse problem in which a piecewise smooth image is recovered from noisy data. The challenging aspect of this problem is to design methods which can selectively filter extraneous information, such as noise, without losing significant features or creating false ones. Many nonlinear models have been proposed for this purpose, however, when an image consists of objects of nonuniform intensity or has been degraded by noise, some of the most successful noise removal techniques which retain and even enhance sharp edges often exhibit a ’stair casing effect’. This can result in the generation of false edges which may in turn yield an incorrect segmentation. Our goal is to study a new model for image restoration which not only removes noise and retains sharp edges, but also avoids stair casing in what should be smooth regions.
Frequently asked questions
About the Deep Learning models
This remains difficult with real world images as the verifying source image is obviously not present. Therefore we output result from several models. Its then up to the user to choose which fits best.
We are constantly reviewing new studies in computer vision to stay on top of the latest technology in the field of image restoration and optimization. We are working on more than one approach to present a certain accuracy with each output result.
One alternative could be to use a downstream vision task e.g. face recognition, to optimize the super-resolution model. But that requires a clients database access where we will face privacy restrictions.
About the manual editing of images
As stated above. Our users have to determine what output fits there need best. Based on that result we edit the image manually. In general no facial structures are added manually. Mostly superficial modifications like skin structure, hair, sharpness, brightness, colorization, etcetera.
In an expert workflow there is the option to review stages of results.
We currently do not offer to restore video files. However, we can assist in frame extraction for the best results for restoring your images.
About the legal side of image restoration for law enforcement
Write the answer to the question here. This way, your visitors can easily pick the questions that matter to them, without being distracted by loads of text from the FAQ.
Countries have different privacy laws, therefore we cannot answer this question. To determine the privacy laws applicable in your country its important to know that a restored image contains new pixels (data) dat have been added by our software that are not present in the source image.
At this point not. However we aim to offer only "state of the art" technology in the field of image restoration with deep learning models.