- #HOW TO BLUR A FACE IN A PHOTO IN GOOGLE PHOTOS SOFTWARE#
- #HOW TO BLUR A FACE IN A PHOTO IN GOOGLE PHOTOS ISO#
This process is repeated at multiple scales, making it robust to artifacts that may arise from compression, image resizing, or other non-linear camera processing operations. To estimate this brightness-based noise level, we sample a number of small patches across the image and measure the noise level within each patch, after roughly removing any underlying structure in the image. That is, for each possible brightness level, the model estimates a corresponding noise level in a manner agnostic to either the actual source of the noise or the processing pipeline.
The noise level is modeled as a function of the brightness of the underlying pixel. So, a camera-agnostic approach for noise reduction begins by formulating a method to gauge the strength of noise at the pixel level from any given image, regardless of the device that created it. In order to accurately process a photographic image and successfully reduce the unwanted effects of noise and blur, it is vitally important to first characterize the types and levels of noise and blur found in the image. These noise and blur reduction strategies are critical components of the recent Google Photos editor updates, which includes “ Denoise” and “ Sharpen” tools that enable users to enhance images that may have been captured under less than ideal conditions, or with older devices that may have had more noisy sensors or less sharp optics.Ī demonstration of the “Denoise” and “Sharpen” tools now available in the Google Photos editor. Both of these components are designed to maximize computational efficiency, so users can successfully enhance the quality of a multi-megapixel image in milliseconds on a mobile device. We developed a pull-push denoising algorithm that is paired with a deblurring method, called polyblur. Today, we present a new approach for camera-agnostic estimation and elimination of noise and blur that can improve the quality of most images.
This interconnectedness increases the difficulty of developing image enhancement techniques that are computationally efficient to run on mobile devices. In most situations, these two problems are intrinsically related: noise reduction tends to eliminate fine structures along with unwanted details, while blur reduction seeks to boost structures and fine details. However, when presented with an image produced from an arbitrary (possibly unknown) camera, improving noise and sharpness becomes much more challenging due to the lack of detailed knowledge and access to the internal parameters of the camera.
#HOW TO BLUR A FACE IN A PHOTO IN GOOGLE PHOTOS SOFTWARE#
It is far easier to minimize the effects of noise and blur within a camera pipeline, where details of the sensor, optical hardware and software blocks are understood. Image blur can be caused by a wide variety of phenomena, from inadvertent camera shake during capture, an incorrect setting of the camera’s focus (automatic or not), or due to the finite lens aperture, sensor resolution or the camera’s image processing. The captured noisy signal is then processed by the camera image processor (ISP) and later may be further enhanced, amplified, or distorted by a photographic editing process. Noise can be associated with the particle nature of light ( shot noise) or be introduced by electronic components during the readout process ( read noise).
#HOW TO BLUR A FACE IN A PHOTO IN GOOGLE PHOTOS ISO#
These are particularly relevant when taking pictures in poor light conditions, where cameras may compensate by increasing the ISO or slowing the shutter speed, thereby exacerbating the presence of noise and, at times, increasing image blur. Posted by Mauricio Delbracio, Research Scientist and Sungjoon Choi, Software Engineer, Google Researchĭespite recent leaps in imaging technology, especially on mobile devices, image noise and limited sharpness remain two of the most important levers for improving the visual quality of a photograph.
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