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The iPhone wallpaper versions are 2000 x 2778 which should give wiggle room to position the backgrounds, see Apple'sChange the wallpaper on your iPhone for instructions on how to set your iPhone wallpaper.

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One of the very frequent tasks computers do is upscaling raster images, aka zooming in or increasing the size of an image. This happens when you zoom in on an image in an application like Preview or Photoshop, or zoom in on a web browser or pinch and zoom on your iPhone. Upscaling has come a long way in the past few years, with machine-learning-assisted upscaling algorithms. These use tricks like taking into account the hue/luminosity (color and its brightness) of surrounding pixels and filling in what it believes the new pixels should be. Before machine-learning, upscaling meant duplicating pixels (nearest neighbor) or duplicating pixels and creating transitions between the hue/luminosity (bilinear). If you have a sharp line, perhaps a sharp mountain silhouette against a sunset (or in my case, a cat whisker against the ground), the machine-learning algorithm will "notice" the sharp contrast between the two areas as it has been "trained" to do so. It will then infer that it should try and keep the sharpness rather than lose the detail, when it creates new pixels to fill the space from upscaling.

While none of these upscales are nearly as good as a high-resolution photo, ML upscaling provides a much better result in a pinch. The ML upscaling is much better at preserving details in the image, such as Mr. Orange's whiskers, his fur, and his pet tag, while providing nice soft results on the areas out of focus. ML (machine learning) upscaling has become popular with modern GPUs. In Windows, AMD and NVidia GPUs can take a videogame rendered at 1440p and upscale it to 4k as it takes less effort for the GPU to upscale the image than it takes to render an image with a lot more pixels, enabling the GPU to churn out higher frame rates and keeping more visual fidelity than using bilinear upscaling. Of course, machine learning isn't limited to uspcaling (or even images).

Machine learning can also be used to provide other types of "best guesses" like denoising images that have artifacts from compression, like JPEGs which are a "lossy format", meaning to save data, they alter the photos to consume less space. Lossy compression is used extensively for media as you do not need a byte-for-byte accurate representation of the original data. Depending on how much compression and what sort of codec (compression type), it will introduce evidence of the compression. Everyone is familiar with the effects of lossy compression, be it an image of low quality, a video with blocky noise, or an audio file that sounds garbled. Machine learning can undo some of this, but it's highly imperfect as it's trying to guess what the original source was trying to convey.

I first tried Gigapixel but ended up just using Pixelmator Pro. Each background was individually treated, using the ML upscaling but with a combination of multiple layers with sharpening, blurs, denoising, grain, and old fashion brush tools on layer masks. As a UX Developer, I split my time between graphics applications and good ol' fashioned coding as my job is to take static pixel images and turn them into code for interfaces for web and mobile applications, although I do often do "full-stack," which is a nice way of saying "everything". I've gone away from Adobe products years ago, with Sketch / Figma / Pixelmator Pro / Final Cut Pro partly out of personal preference and following the industry.

Each image was hand treated, although some images had better sources than others. I'm sure another artist with more time could bring these even better to life as I tried to keep roughly an hour-ish per image (as a UX developer, time budgets are a harsh reality for me). If you create your own versions, please reach out to me, as I'll happily link them.

Taken together, the studies using fNIRS and fMRI so far have greatly advanced our understanding of the neural substrate of the mood-improving effects of contact with nature and have specifically identified a prominent role of the PFC. However, which specific regions within the PFC underlie this benefit and serve as the online neural substrates in particular remain unclear. fMRI is considered inappropriate for investigating the sensitive affective benefits of contact with nature and the online neural substrates due to its heavy noise and severe constraint on the subjects. fNIRS is more appropriate, however, previous studies have generally used models of fNIRS that provide only a single measurement for the broad area of the PFC (e.g., 2-channel NIRS). Therefore, in the present study, we aimed to identify the specific regions involved in the mood-improving effect of viewing images of nature using a 52-channel NIRS instrument (ETG-4000; Hitachi Medical Co., Tokyo, Japan).

The effect of image viewing on brain activity. Green and black lines indicate the mean oxy-Hb concentration while viewing images of nature and city, respectively; green and black shadows indicate the standard error.

Although we predicted that both the OFC and the dlPFC are involved in the mood-improving effect of viewing images of nature, only the involvement of the OFC is confirmed here. The OFC is well-known for playing an essential role in value representation and affective processing through integrating information from sensory cortices, brain stem autonomic areas, and the amygdala [32,33]. The OFC has reciprocal projections with the amygdala, another area important for evaluating emotionally salient (in particular aversive, unpleasant) information [49]. The reduced activity in the right OFC in the present study might reflect reduced activity in the amygdala, which has been identified after contact with nature in fMRI studies (for a more extensive discussion, see [11]). Notably, the OFC has been implicated in the pathophysiology of depression and anxiety [50,51]. Patients with depression [50] and anxiety [51] demonstrate increased activity in the OFC at rest, which is attenuated after pharmacological treatment. The abnormally increased activity in the OFC is considered to be a result of a hyperactive amygdala in these patients. Thus, one may speculate that viewing images of nature or other means of contact with nature might have therapeutic effects for these patients by modulating the activity in the OFC (and potentially the amygdala). It deserves future research to test this speculation with patients.

Nature photography is a wide range of photography taken outdoors and devoted to displaying natural elements such as landscapes, wildlife, plants, and close-ups of natural scenes and textures. Nature photography tends to put a stronger emphasis on the aesthetic value of the photo than other photography genres, such as photojournalism and documentary photography.[1]

Landscape photography is one of the categories of photography often associated with nature photography. It focuses on images of the natural world (such as rivers, mountains, deserts, and forests) [2] as well as man-made structures (such as city skylines). However, that is rarer and separated from nature photography. As such, landscape photography is an adjacent rather than a sub-category of nature photography.

As is the trend in much of nature photography, the focus of landscape photography is on the natural beauty of the world with little artificial lighting or staging.[3] There are also forms of landscape photography that are seen as more artistic or abstract than others, though those seem to lean more towards a macro photography style.

Wildlife photography is all about capturing pictures of animals, especially those considered exotic, in their natural habitats, and so only became truly popular once cameras were portable.[4] Depending on the purpose of the photograph and photographer, Wildlife photography can either portray the animals in action (such as eating, fighting, or in flight), or in more static and detailed poses for identification purposes. Much like in landscape photography, wildlife photography is also often used in magazines such as National Geographics to inform and inspire audiences.Photographs taken of captive or controlled animals are not considered wildlife photography as by definition from the Photographic Society of America, the Fdration Internationale de l'Art Photographique and the Royal Photographic Society. According to these three worlds largest photography societies the definition for wildlife photography, that will be applied to photography competitions, is photos taken of any zoological of biological organism (including fungi and algae), in a uninhibited (wild) environment.

In the mid-20th century, wildlife photography began to gain wider recognition as a legitimate form of artistic expression. Photographers like Peter Beard and Art Wolfe began to use wildlife photography as a means of conservation, using their images to raise awareness about the need to protect endangered species and their habitats.[9][10]

Many photographers record images of the texture in a stone, tree bark, leaf, or any of other small scenes. Many of these images are abstract.[14] Tiny plants and mushrooms are also popular subjects. Close-up nature photography doesn't always need a true macro lens; however, the scenes here are small enough that they are generally considered different from regular landscapes.

Color images are not a requirement of nature photography. Ansel Adams is famous for his black-and-white depictions of nature, while Galen Rowell praised Fujifilm Velvia film for its bright, saturated colors, asking "Who wants to take dull pictures that will last a hundred years?"[20] Both men distinguish between photography as an expressive art form and sensitometry; an accurate reproduction is not necessary. 006ab0faaa